Method for providing orthodontic treatment

ABSTRACT

A method for orthodontic treatment is provided. The method comprises obtaining feedback data including patient feedback data points collected by fitting an orthodontic appliance configured to apply a force to a tooth of a patient and indicating a level of discomfort experienced by the patient, wherein the force is a function of a critical force that is specific to the patient, correlating the level of discomfort to the force applied to the tooth of the patient, and, determining whether or not the feedback data is optimized for determining a target orthodontic force for orthodontic treatment that is an optimal orthodontic force based on the critical force.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. patent application Ser. No. 17/143,907,filed on 7 Jan. 2021, the entire contents of which is incorporatedherein by reference in its entirety.

FIELD OF THE INVENTION

This invention relates to methods for providing orthodontic treatment.

BACKGROUND TO THE INVENTION

Orthodontics is the branch of dentistry that deals with the preventionor correction of irregularities of the teeth and jaws. Theseirregularities may affect the oral health, as well as possibly thephysical, aesthetic and/or mental wellbeing of affected individuals.

Tooth positioning and orofacial bone structures can be altered usingmanual, mechanical systems, generally consisting of a combination of,for example, wires, brackets, bands, chains, springs and elastics, asystem commonly referred to as “dental braces” or simply “braces”.Similarly, orthodontic treatment and repositioning of tooth and bonestructures can be achieved using one or more aligners, retainers, or acombination thereof. Braces or aligners are commonly used to generate,transmit and maintain forces, force vectors and moments to individualteeth or between teeth, activating various biomechanical processeswithin the affected tissues to facilitate tooth movement.

It is generally accepted that a force of zero magnitude will not induceany tooth movement, whereas a force of excessive magnitude might damagecells surrounding the tooth and may also cause root resorption andexcessive patient discomfort. This gives rise to the concept that an“optimal force” or “optimal orthodontic force” exists, between a zeroforce and a force of excessive magnitude, which would be capable ofinducing the maximum rate of tooth movement without causing any tissuedamage, root resorption, as little as possible patient discomfort, and aminimum level of additional, adverse side-effects.

Conventional orthodontic systems have a number of shortcomings withregard to this optimal orthodontic force. Firstly, the majority oforthodontic systems rely purely on mechanical components, which allowfor little or no control once put in place. The placement of themechanical components by the practitioner largely determines the forcesexerted on the teeth and virtually no controlled changes can be madethereto without manually changing the configuration of the mechanicalcomponents. Furthermore, many of the components used, which as statedabove include, amongst others, springs, wires and elastic bands, do notaccurately generate a constant desired force over a longer period oftime or over a specified distance, largely due to the physicalcharacteristics of these components. This makes it improbable that theforces transmitted to the teeth are representative of the optimal forcefor any continuous period of time. The result of other than optimalforces being applied to the orofacial structures can induce the problemsmentioned above.

Conventionally, the ability to control the forces transmitted to theteeth by an orthodontic appliance was one of the biggest challenges. Theavailability of new technologies and advanced manufacturing techniqueshas greatly improved the ability to control such parameters. However,this gives rise to the next and perhaps even more critical question inorthodontics—the need to accurately determine the optimal force thatwould result in the most effective treatment. For example, Wu et al.,have conducted “A biomechanical case study on the optimal orthodonticforce on the maxillary canine tooth based on finite element analysis”.Liao et al. present “Biomechanical investigation into the role of theperiodontal ligament in optimizing orthodontic force: a finite elementcase study”. Such an optimal force is highly patient and also toothspecific and the ability to accurately determine such an optimal forceis key to provide customized and patient specific orthodontic treatment.To the applicant's knowledge, however, it has not yet been possible toquantitatively describe such an optimal force. Finite elementmethod-based analysis, for example, requires careful definition of inputparameters which, for the case of the PDL complex, should preferablyinclude parameters detailing blood pressure, fibrous tissue structureand bone properties and structures, which naturally vary considerablyfrom one person to the next. Without proper definition of theseparameters, the models cannot be used to determine an optimalorthodontic force that is patient- and tooth-specific.

Based on the data from a number of studies of which the applicant isaware, it was concluded that the reviewed experimental results werenegatively affected by, amongst others, the inability to accuratelycalculate stresses in the periodontal ligament of a given tooth, theinability to control the type of tooth movement, the different phases oftooth movement during an applied force and large inter-individualvariations or even variations within individuals. As a result, no exactideal force magnitude could be recommended.

It has also been found that large individual variations exist for themean rate of tooth movement achieved under application of the sameforces. A possible explanation that has been proposed for thisphenomenon is that each individual could have his or her own optimalforce that would produce the maximum rate of tooth movement.

Generally, the view has been adopted that the movement of teeth is aresult of externally applied mechanical stimuli and the subsequentbiological reactions that take place within the periodontium. Inherentto the mechanical stimuli are various parameters including the forcemagnitude, direction, point of application, frequency of application andduration of application. Still further parameters could play animportant role when non-static forces are considered such as the forceprofile, oscillatory frequency and oscillatory amplitude. The aboveparameters in combination with the anatomical and physiologicalproperties inherent to the affected tooth/teeth lead to yet furtherfactors affecting tooth movement. A certain mechanical stimulus appliedto a specific case will lead to cellular strains, shear stresses andpressure changes within the affected tissues. Each of these couldfurther affect the resulting tooth movement thereby emphasizing theimportance of the externally applied stimulus.

There is accordingly scope for improvement.

In the remainder of this specification the terms “optimal orthodonticforce” should be interpreted to be such an optimal force when applied inan orthodontic environment. The term “optimal force” should in turn beinterpreted to have a corresponding meaning but capable of being appliedin any reconstructive or corrective surgery where relative bone ortissue movement is achieved by means of the application of a mechanicalforce or moment over a period of time. In addition, the terms “force”and “stimulus” are used interchangeably and should be interpretedbroadly to include any combination of forces and moments or eitherindividually.

The preceding discussion of the background to the invention is intendedonly to facilitate an understanding of the present invention. It shouldbe appreciated that the discussion is not an acknowledgment or admissionthat any of the material referred to was part of the common generalknowledge in the art as at the priority date of the application.

SUMMARY OF THE INVENTION

In accordance with an aspect of the invention there is provided a methodcomprising: obtaining feedback data including patient feedback datapoints collected by fitting an orthodontic appliance configured to applya force to a tooth of a patient and indicating a level of discomfortexperienced by the patient, wherein the force is a function of acritical force that is specific to the patient; correlating the level ofdiscomfort to the force applied to the tooth of the patient; and,determining whether or not the feedback data is optimized fordetermining a target orthodontic force for orthodontic treatment that isan optimal orthodontic force based on the critical force.

The critical force may be based on periodontal ligament (PDL) behaviordata. The level of discomfort may be indicated on a scale with relationto the force as a function of the critical force.

The feedback data may be obtained during use or testing of theorthodontic appliance. The feedback data may be based on or include rateof change data points measured while using the orthodontic appliance.The feedback data may include treatment feedback data points having beenobtained from use of the orthodontic appliance.

The optimal orthodontic force may achieve the least level of discomfortwhile still resulting in orthodontic tooth movement. The optimalorthodontic force may optimize a balance of patient comfort andtreatment outcome.

The method may be repeated to obtain updated feedback data until thefeedback data points of the feedback data are optimized. The method mayinclude determining the target orthodontic force by inputting thecritical force into an algorithm. The algorithm may include one or moreof time, magnitude, patient characteristic and patient treatmentcomponents.

The method may include using the target orthodontic force to determineone or both of a stage force value and a stage movement value for one ormore stages of orthodontic treatment. The method may include using thetarget orthodontic force to determine configuration parameters forconfiguring an orthodontic appliance to apply a force which approximatesthe target orthodontic force. The force that approximates the targetorthodontic force may be a stage force value. The target orthodonticforce may be determined by inputting the critical force into analgorithm, the method may include: if the feedback data is notoptimized, updating the algorithm for determining an updated targetorthodontic force; or, if the feedback data is optimized, storing thealgorithm as an optimal algorithm for use in determining a targetorthodontic force that is the optimal orthodontic force.

The updated target orthodontic force may be used to reconfigure theappliance or create a new appliance configured to apply the updatedtarget orthodontic force. Updating the algorithm may iteratively updatethe algorithm to optimize the feedback data. Updating the algorithm mayinclude adjusting weights of one or more components or variables of thealgorithm.

In accordance with a further aspect of the invention there is provided amethod comprising: receiving patient data including patient treatmentdata and patient characteristic data, the patient treatment data atleast including one or both of a tooth type indicator and a toothposition indicator associated with a tooth of a patient to be treatedand a treatment movement value which describes required tooth movementto effect treatment of the tooth; determining one or more stage movementvalues and corresponding stage force values, wherein each stage forcevalue is based on a target orthodontic force value that is based on oneor more determinate points of periodontal ligament (PDL) behavior dataassociated with at least a subset of the patient data, wherein the PDLbehavior data includes or is based on force measurement data points andcorresponding displacement measurement data points having been measuredin-vivo while applying a force to a particular tooth of a human oranimal subject, wherein the sum of the one or more stage movement valuesapproximates the treatment movement value; and, outputting the one ormore stage movement values and corresponding stage force values as atreatment plan for treatment of the tooth.

Determining one or more stage movement values and corresponding stageforce values may include: determining a target orthodontic force valuebased on one or more determinate points of PDL behavior data associatedwith at least a subset of the patient data, wherein the PDL behaviordata includes or is based on force measurement data points andcorresponding displacement measurement data points having been measuredin-vivo while applying a force to a particular tooth of a human oranimal subject; using the target orthodontic force value to determine acorresponding stage movement value; comparing the corresponding stagemovement value to the treatment movement value; and, if thecorresponding stage movement value is less than the treatment movementvalue, determining, for a next treatment stage, a next targetorthodontic force value and a corresponding next stage movement valueuntil the sum of stage movement values approximates the treatmentmovement value.

Determining one or more stage movement values and corresponding stageforce values may include: initializing a stage movement value beingequal to the treatment movement value; using the stage movement value todetermine a corresponding stage force value based on PDL behavior dataassociated with at least a subset of the patient data, wherein the PDLbehavior data includes or is based on force measurement data points andcorresponding displacement measurement data points having been measuredin-vivo while applying a force to a particular tooth of a human oranimal subject; comparing the corresponding stage force value to atarget orthodontic force value based on one or more determinate pointsof the PDL behavior data; if the corresponding stage force value exceedsthe target orthodontic force value, successively defining a new stagemovement value which is less than the previous stage movement value anddetermining a new corresponding stage force value based on the PDLbehavior data until the corresponding stage force value approximates thetarget orthodontic force value.

The method may include storing the stage movement value that correspondsto the stage force value that approximates the target orthodontic forcevalue for use in a treatment plan. The method may include determining anumber of treatment stages by dividing the treatment movement value bythe stage movement value so as to determine the number of stagesrequired to effect treatment of the tooth by applying the targetorthodontic force value for each stage.

Each of: the treatment movement value, stage movement value, stage forcevalue and target orthodontic force value may include components for eachof six degrees of freedom. Each of: the treatment movement value, stagemovement value, stage force value and target orthodontic force value maybe time dependent.

Receiving patient data may include determining the treatment movementvalue for the patient. The method may be conducted for each tooth to betreated such that a case-, tooth- and patient-specific stage movementvalue and corresponding stage force value are determined for each toothto be treated.

The method may include determining the target orthodontic force value.The PDL behavior data may include a PDL behavior model, and determiningthe target orthodontic force value may include: using the PDL behaviormodel to determine one or more determinate points of the PDL behaviordata; and, inputting the one or more determinate points into analgorithm that determines the target orthodontic force as a function ofthe one or more determinate points. The method may include determiningthe target orthodontic force value each time a new stage movement valueis defined.

Further inputs to the algorithm may include data points relating to oneor more of time, magnitude, patient characteristic and patient treatmentcomponents.

The PDL behavior model may be trained using measured PDL behavior data.The measured PDL behavior data may include force measurement data pointsand corresponding displacement measurement data points having beenmeasured in-vivo while applying a force to a tooth of a human or animalsubject.

The algorithm may be optimized by iteratively updating the algorithm tooptimize feedback data such that the target orthodontic force is anoptimal orthodontic force. The feedback data may include one or more of:test rig feedback data points, patient feedback data points andtreatment feedback data points. The feedback data may be obtained duringuse or testing of an orthodontic appliance configured to apply a targetorthodontic force.

In accordance with another aspect of the invention there is provided asystem comprising: a processor and a memory configured to providecomputer program instructions to the processor to execute functions ofmodules; a patient data receiving module for receiving patient dataincluding patient treatment data and patient characteristic data, thepatient treatment data at least including one or both of a tooth typeindicator and a tooth position indicator associated with a tooth of apatient to be treated and a treatment movement value which describesrequired tooth movement to effect treatment of the tooth; a stage valuedetermining module for determining one or more stage movement values andcorresponding stage force values, wherein each stage force value isbased on a target orthodontic force value that is based on one or moredeterminate points of periodontal ligament (PDL) behavior dataassociated with at least a subset of the patient data, wherein the PDLbehavior data includes or is based on force measurement data points andcorresponding displacement measurement data points having been measuredin-vivo while applying a force to a particular tooth of a human oranimal subject, wherein the sum of the one or more stage movement valuesapproximates the treatment movement value; and, a stage value outputmodule for outputting the one or more stage movement values andcorresponding stage force values as a treatment plan for treatment ofthe tooth.

In accordance with another aspect of the invention there is provided acomputer program product comprising a non-transitory computer-readablemedium having stored computer-readable program code for performing thesteps of: receiving patient data including patient treatment data andpatient characteristic data, the patient treatment data at leastincluding one or both of a tooth type indicator and a tooth positionindicator associated with a tooth of a patient to be treated and atreatment movement value which describes required tooth movement toeffect treatment of the tooth; determining one or more stage movementvalues and corresponding stage force values, wherein each stage forcevalue is based on a target orthodontic force value that is based on oneor more determinate points of periodontal ligament (PDL) behavior dataassociated with at least a subset of the patient data, wherein the PDLbehavior data includes or is based on force measurement data points andcorresponding displacement measurement data points having been measuredin-vivo while applying a force to a particular tooth of a human oranimal subject, wherein the sum of the one or more stage movement valuesapproximates the treatment movement value; and, outputting the one ormore stage movement values and corresponding stage force values as atreatment plan for treatment of the tooth.

Further features provide for the computer-readable medium to be anon-transitory computer-readable medium and for the computer-readableprogram code to be executable by a processing circuit.

Embodiments of the invention will now be described, by way of exampleonly, with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1A is a schematic diagram which illustrates an example orthodontictreatment system according to aspects of the present disclosure;

FIG. 1B is a schematic diagram which illustrates an example measurementsystem according to aspects of the present disclosure;

FIG. 2A is a schematic diagram which illustrates a first exampleembodiment of an attachment mechanism according to aspects of thepresent disclosure;

FIG. 2B is a schematic diagram which illustrates a second exampleembodiment of an attachment mechanism according to aspects of thepresent disclosure;

FIG. 3A is a schematic diagram which illustrates a reference jigincluding target points for cooperation with a position measurementmodule according to aspects of the present disclosure;

FIG. 3B is a schematic diagram which illustrates a field of view of acamera of a position measurement module according to aspects of thepresent disclosure;

FIG. 3C is a schematic diagram which illustrates an example use ofgyroscopes in the measurement system of FIG. 1B;

FIG. 4A is a schematic diagram which illustrates one example embodimentof a user interface according to aspects of the present disclosure;

FIG. 4B is a schematic diagram which illustrates another exampleembodiment of a user interface according to aspects of the presentdisclosure;

FIG. 5 is a flow diagram which illustrates an example embodiment of amethod for measuring data points relating to force applied to and/orposition of a part of a human or animal subject relative to other partsof the human or animal subject according to aspects of the presentdisclosure;

FIG. 6 is a schematic diagram which illustrates elements of a feedbacksystem according to aspects of the present disclosure;

FIG. 7 is a flow diagram which illustrates steps for one exampleuse-case for determining a biomechanical tissue response using themeasurement device described herein;

FIG. 8 is a flow diagram which illustrates an example method fordetermining the neutral position of a tooth in in biomechanical responsedata;

FIG. 9 is a flow diagram which illustrates the steps required in onepossible scenario to measure the biomechanical tissue response with theuser applying a random stimulus to one or more teeth;

FIG. 10 is a flow diagram which illustrates an example method forproviding real-time feedback based on measurement data;

FIG. 11 is a flow diagram which illustrates an example method fordetermining or calculating a critical force according to aspects of thepresent disclosure;

FIG. 12 shows a possible displacement of a tooth within the alveolusresulting from an applied force F;

FIG. 13 show the different locations of the Centre of Resistance of atooth depending on the tooth and root morphology;

FIG. 14 shows the same force F applied at different points on the toothand in different directions;

FIG. 15 illustrates side profile views of a tooth showing a non-uniformthickness of the PDL and localized differences in alveolar bonemorphology or tooth root morphology;

FIG. 16 is a side profile of a tooth in a position before a stimulus hasbeen applied to the tooth and after a stimulus has been applied to thesame tooth;

FIG. 17 is a side profile view of a tooth showing different points ofmeasurement, force application and center of rotation on the tooth;

FIG. 18 shows different values of the critical force level Fc determinedat different times during orthodontic treatment for three tooth types, amolar tooth, a canine tooth, and an incisor tooth;

FIG. 19A shows an example of raw data measured from the biomechanicaltissue response resulting from an applied stimulus to a tooth, as wellas an average curve describing the force displacement relationship for aback-and-forth movement of the same tooth;

FIG. 19B shows another example of raw data measured from thebiomechanical tissue response resulting from an applied stimulus to atooth;

FIG. 19C shows yet another example of raw data measured from thebiomechanical tissue response resulting from an applied stimulus to atooth;

FIG. 20 shows an example of the biomechanical tissue response resultingfrom an applied stimulus measured at two different points on the tooth;

FIG. 21 shows an example of the biomechanical tissue response resultingfrom a repeated cyclic stimulus applied to a tooth, each cycle leadingto an increase in tooth displacement for the same force level;

FIG. 22 shows an example of the biomechanical tissue response resultingfrom a repeated cyclic stimulus applied to a tooth, each cycle showing adifferent response due to the viscous and fluid effects of the PDL;

FIG. 23 shows an example of the biomechanical tissue response with adifferent response for a stimulus in opposing direction;

FIG. 24 shows an example of segmented linear regression being applied tothe biomechanical tissue response, each line segment representing theinitial tooth movement (ITM) and secondary tooth movement (STM), andallowing determinate points on the biomechanical tissue response curveto be defined;

FIG. 25 shows an example of the mathematical processing of thebiomechanical tissue response curve allowing the quantitativedescription of the behavior of the PDL;

FIG. 26 illustrates a schematic diagram of an example user interface forreviewing measurement data obtained using the systems, methods anddevices described herein;

FIG. 27 illustrates a schematic diagram of another example userinterface for reviewing measurement data obtained using the systems,methods and devices described herein;

FIG. 28 illustrates a schematic diagram of yet another example userinterface for reviewing measurement data obtained using the systems,methods and devices described herein;

FIG. 29 is a flow diagram which illustrates an example method fordetermining a target orthodontic force according to aspects of thepresent disclosure;

FIG. 30 shows time-dependent target orthodontic force values for each ofa series of appliances for two different teeth;

FIG. 31 shows a continuous definition of target orthodontic force valuesaccording to aspects of the present disclosure;

FIG. 32 is a flow diagram which illustrates an example method forupdating an algorithm using feedback data according to aspects of thepresent disclosure;

FIG. 33 shows an example embodiment for obtaining patient feedback datapoints using a linear scale on which the level of discomfort isindicated with relation to a target orthodontic force value;

FIG. 34 illustrates an example embodiment of a method for comparingforces according to aspects of the present disclosure;

FIG. 35 shows an example embodiment of a method for training a model foruse in determining PDL behavior data points according to aspects of thepresent disclosure;

FIG. 36 shows an example method for using a PDL behavior model todetermine movement data points according to aspects of the presentdisclosure;

FIG. 37A is a flow diagram which illustrates one example embodiment of amethod for orthodontic treatment staging according to aspects of thepresent disclosure;

FIG. 37B is a flow diagram which illustrates a first example embodimentof a method for determining one or more stage movement values andcorresponding stage force values based on a target orthodontic forcevalue according to aspects of the present disclosure;

FIG. 37C is a flow diagram which illustrates a second example embodimentof a method for determining one or more stage movement values andcorresponding stage force values based on a target orthodontic forcevalue according to aspects of the present disclosure;

FIG. 38 is a chart which illustrates plots of a stage force value andassociated target orthodontic force value over time for a plurality ofstages of orthodontic treatment according to aspects of the presentdisclosure;

FIG. 39 is a flow diagram which illustrates one example embodiment of amethod for orthodontic appliance configuration according to aspects ofthe present disclosure

FIG. 40 is a schematic cross-section through two example orthodonticappliances according to aspects of the present disclosure;

FIG. 41 is a schematic diagram which illustrates an example embodimentof an orthodontic apparatus according to aspects of the presentdisclosure;

FIG. 42 is a flow diagram which illustrates an example method forevaluating configuration parameters according to aspects of the presentdisclosure;

FIG. 43 is a block diagram which illustrates exemplary components whichmay be provided by an orthodontic treatment system according to aspectsof the present disclosure; and

FIG. 44 illustrates an example of a computing device in which variousaspects of the disclosure may be implemented.

DETAILED DESCRIPTION WITH REFERENCE TO THE DRAWINGS

Aspects of the present disclosure relate generally to the field oforthodontic treatment, such as via orthodontic treatment plans andorthodontic appliances. In particular, aspects of the present disclosurerelate to an evidence-based approach to the formulation of orthodontictreatment plans and the configuration of orthodontic appliances forimproved or even optimal orthodontic treatment outcomes. Thisevidence-based approach makes use of data points that are obtained froma large number of human or animal subjects in-vivo as well as variousother data points, such as metadata relating to the relevanthuman/animal subject, feedback data (for example including patientfeedback and treatment progress/outcome feedback) and the like. The datapoints can be compiled into a large-scale dataset for processing,optionally together with feedback data, to output models and/oralgorithms for use in formulating treatment plans and configuringorthodontic appliances for optimal treatment outcomes. The data pointscan include periodontal ligament (PDL) behavior data points which can beused to model estimated or expected PDL behavior and determine anoptimal orthodontic force (also termed “target orthodontic force”herein), tooth movement and treatment parameters based on the estimatedor expected behavior of the PDL. Aspects of the present disclosure aimto build upon existing orthodontic appliance technology usingevidence-based inputs for optimal orthodontic treatment outcomes thatcan be patient, tooth and/or case-specific.

FIG. 1A is a schematic diagram which illustrates an example orthodontictreatment system (1) according to aspects of the present disclosure. Thesystem (1) includes a data processing subsystem (3), a plurality ofpoint of care subsystems (5) and a plurality of measurement sub systems(10).

The measurement subsystems (10) are configured to obtain measurementdata from a large number of human or animal subjects, including forcemeasurement data and position measurement data which together describePDL behavior of the human or animal subject from which the data ismeasured. The measurement subsystems (10) are configured to obtainmeasurement data describing PDL behavior when subjected to an appliedstimulus. The measurement subsystems (10) may further be configured toobtain metadata associated with the measurement data and/or relevanthuman or animal subject. The measurement subsystems (10) transmit themeasurement data and metadata to the data processing subsystem (3) forstorage, processing and/or or analysis. Transmission may be directtransmission or via a communication network (21). It should beappreciated that the measurement systems may be geographicallydistributed over large distances so as to obtain measurement data from avast variety of human or animal subjects. As will be explained ingreater detail below, each measurement subsystem is configured forlow-friction, simple and convenient collection of measurement data tofacilitate the compilation of a large-scale collection of measurementdata.

The data processing subsystem (3) includes a datastore (7) in whichvarious sets or collections of data may be stored. The datastore (7) mayfor example store PDL behavior data (9) including measurement data (9A),for example received from the measurement subsystems (10), and/ormodelled data, for example in the form of one or more PDL behaviormodels (33) having been trained on measurement data and associatedmetadata.

The measurement data (9A) may include a large number of subsets ofmeasurement data, each subset relating to an individual tooth of anindividual human or animal subject. The measurement data may for exampleinclude timestamped force measurement data points and correspondingposition or displacement measurement data points. Each data point, orvalue, may have a component for each of six degrees of freedom. Eachsubset of measurement data is associated with the tooth (e.g., by virtueof tooth type and/or tooth position indicator) with respect to which thedata was measured. The measurement data (9A) may be stored inassociation with the human or animal subject to which it relates (e.g.,using an appropriate identifier, key value or the like) such that it canbe linked to that human or animal subject and/or to metadata (11)related to that human or animal subject. For example, each subset ofmeasurement data may be associated with an identifier of the human oranimal subject with respect to which the data was obtained, for linkingwith associated metadata relating to characteristics of the human oranimal subject.

The one or more PDL behavior models (33) may be configured to accept asinput either an applied force value or a required tooth displacementvalue and to output the other of a simulated tooth displacement valueresulting from the applied force value or simulated required force valueto achieve the required tooth displacement value. The one or more PDLbehavior models may further accept as input patient data includingpatient characteristic data such that the output simulated values arecase, tooth and patient specific.

The datastore (7) may further store metadata (11) associated with thePDL behavior data, which may also be received from the measurementsubsystems. The metadata may include data points, datasets and/orinformation relating to one or more of: parameters affecting the PDLbehavior, such as data points relating to one or more of tooth and rootmorphology, PDL thickness and shape of the human or animal subject(including e.g. X-ray data and/or CT scan data) and the like; healthrelated data such as data points relating to one or more of bloodpressure, body mass index, weight, oral and tissue health, time intreatment journey (if applicable), smoker/non-smoker status, dentalhistory (including e.g. information on root canal history) of the humanor animal subject and the like; data points relating to one or moreliving standards measure (LSM) inputs, an LSM output, country, state,city of residence and/or birth, diet, age, gender, ethnicity and speciesof the human or animal subject and the like. The metadata may be storedin association with the human or animal subject to which it relates(e.g., using an appropriate identifier, key value or the like) such thatit can be linked to the measurement data obtained from that human oranimal subject. The metadata may therefore include data points(including e.g., scores or other indicators) relating to physiological,biological, genetic and situational characteristics of the human oranimal subject from whom measurement data is obtained.

In some embodiments, the datastore stores one or more of patient data(13), feedback data (15), target orthodontic force data (17) and thelike. In other embodiments, other datastores are provided for the othercategories of data needing to be stored.

Patient data (13) may be stored or otherwise accessible to the systemfor each patient to be treated. Patient data may include patienttreatment data and patient characteristic data. The patient data may beassociated with the patient to which it relates (e.g., using anappropriate identifier, key value or the like).

Patient treatment data may include data points relating to patientcondition and treatment requirements, including for example one or bothof a tooth type indicator and a tooth position indicator associated witha tooth or each tooth of the patient to be corrected. The tooth typeindicator may be one of: incisor, canine, premolar, and molar, and thetooth position indicator indicates the position of the tooth in themouth of the patient (e.g. using a standardized numbering convention toallow comparison of like teeth from one person to the next). The patienttreatment data may also include treatment movement data (e.g. includingone or more treatment movement values) which describes the requiredtooth movement to effect treatment (or correction) of the tooth. Eachtreatment movement value may have a component for each of six degrees offreedom. A treatment movement value may for example describe therequired translation of the tooth in millimeters along the x-, y- andz-axis and the required rotation of the tooth in degrees around each ofthe x-, y-, and z-axis. Where multiple teeth are to be corrected,patient treatment data may be provided for each tooth to be corrected orfor one or more groupings of teeth to be corrected.

Patient characteristic data may include, for each patient, data points,datasets and/or information relating to one or more of: parametersaffecting the PDL behavior, such as data points relating to one or moreof tooth and root morphology, PDL thickness and shape of patient(including e.g. X-ray data and/or CT scan data) and the like; healthrelated data such as data points relating to one or more of bloodpressure, body mass index, weight, oral and tissue health, time intreatment journey (if applicable), smoker/non-smoker status, dentalhistory (including e.g. information on root canal history) of thepatient and the like; data points relating to one or more livingstandards measure (LSM) inputs, an LSM output, country, state, city ofresidence and/or birth, diet, age, gender and ethnicity patient and thelike. Patient characteristic data for each patient may therefore includedata points relating to physiological, biological, genetic andsituational characteristics of the patient.

The data processing system (3) receives data from the one or moremeasurement subsystems (10) and optionally from the one or more point ofcare subsystems (3). The data processing system (3) may receive datafrom the one or more measurement subsystems (10) via a data input module(19) and optionally a suitable communication network (21), such as theInternet. The data input module (19) may perform various datastandardization, cleaning and formatting requirements as may be requiredbefore persisting or storing the received data into the datastore. Thedata from the one or more point of care subsystems (3) may be receivedvia one or more interface modules (23), which may be provided by one ormore suitable APIs, and the communication network (21). The interfacemodule (23) facilitates interacting and integration with othersubsystems, such as the point of care subsystems. The interface modulemay be provided for consumption by the point of care subsystems for theexchange of commands, instructions, requests and data between thesubsystems.

In the illustrated embodiment, the data processing subsystem (3)includes an optimal force engine (25) which has access to the datastore(7) and is configured to determine a target (also termed “optimal”)orthodontic force based on one or more determinate points of relevantPDL behavior data (9). The optimal force engine (25) may access datafrom the datastore (7), such as PDL behavior data (9) or one or moredeterminate points thereof, and process the data to define a target oroptimal orthodontic force based on the one or more determinate pointsand a function which is defined by an algorithm that is maintained andupdated by the optimal force engine (25). The optimal force engine mayretrieve data that based on patient data that the optimal force enginereceives, for example from a point of care subsystem, a treatment planengine (41), an appliance configuration engine (43), the interfacemodule (23) or the like. In some implementations, the optimal forceengine (25) defines the target orthodontic force based on a determinatepoint being a critical orthodontic force. The optimal force engine (25)is configured to determine the target orthodontic force in relation to acase-specific soft-tissue response of the PDL and the related criticalorthodontic force. In some cases, the target orthodontic force isdefined as a function of the critical orthodontic force, which isdependent on multiple physiological factors and, as described in greaterdetail below, may be determined in-vivo. Other implementations use otherdeterminate points. Determining the target orthodontic force may includedetermining the tissue response of the periodontal ligament, identifyinga determinate point along a force-displacement curve and determining anoptimal force as a function of said determinate point on theforce-displacement curve (e.g., using an optimized algorithm). Theoptimal force engine (25) maintains and updates the algorithm based onfeedback data (15) including one or more of patient feedback datapoints, treatment feedback data points and test rig feedback data pointsfor outputting a target orthodontic force that can be considered optimalfor the given input determinate data.

In the illustrated embodiment, the data processing subsystem (3)includes a model training engine (31) which is configured to train andoutput one or more models, such as one or more PDL behavior models (33).The models may be mathematical models. The model training engine (31)may access data from the datastore (7), such as PDL behavior data (9)including one or both of measured PDL behavior data (9A) and modelledPDL behavior data (e.g., previously trained PDL behavior models and/orsynthetic data which can be generated by other means than in-vivomeasurements and could be used to develop models), and process the datato output one or more mathematical models including for example one ormore PDL behavior models (33). In some implementations, the modeltraining engine is configured to model the behavior of the PDL based atleast to some extent on the in-vivo measurements of said PDL behaviordata. In some implementations, as will be explained in greater detailherein, this includes acquiring for a plurality of teeth of a number ofindividuals measurement data describing PDL behavior when subjected toan applied stimulus; using mathematical models and machine learningtechniques to identify features predictive of the behavior of the PDLand training a machine learning (or other suitable) model to produce anestimate of the behavior of the PDL and the resulting tooth displacementwhen subject to an applied stimulus. The model training engine (31) maythus be configured to output one or more models estimating the behaviorof the PDL based at least to some extent on the in-vivo measurements ofsaid PDL for use in modelling patient, case and tooth specific PDLbehavior and optionally in determining a target or optimal orthodonticforce, tooth movement and treatment parameter based on the estimatedbehavior of the PDL. The model training engine (31) may implement one orboth of a method for modelling periodontal ligament behavior and amethod of assimilating patient- and case-specific values of a criticalorthodontic force.

In the illustrated embodiment, the data processing subsystem (3)includes a treatment plan engine (41) and an appliance configurationengine (43) which are accessible to the point of care subsystems (5) viathe interface module (23) and communication network (21). The treatmentplan engine (41) may be configured to access the optimal force engine(25) and PDL behavior data, including the PDL behavior model (33), forimplementing a method for orthodontic staging using critical orthodonticforce parameters or other suitable PDL-based determinate points. Theappliance configuration engine (43) may be configured to access theoptimal force engine (25) and PDL behavior data, including the PDLbehavior model (33), to implement a method for orthodontic applianceconfiguration, including designing an orthodontic appliance andpreparing a specification for, designing, and producing an orthodonticappliance (such as a clear aligner) using critical orthodontic forceparameters or other suitable PDL-based determinate points. Aspecification for an orthodontic appliance may include one or moreconfiguration parameters which define configuration of the relevantappliance so as to apply a patient, case and tooth specific force thatis optimized for patient feedback and treatment outcome.

Each of the point of care subsystems (5) may include any suitableinfrastructure accessible to healthcare professionals for the purpose oforthodontic treatment planning and orthodontic appliance configuration,specification and/or design. Each point of care subsystem may forexample include a computing device (tablet, laptop, desktop computer,etc.) via which a healthcare professional can interact with theappliance configuration engine (43) and/or treatment plan engine (41) togenerate a treatment plan that is patient, tooth and case specific andto configure for manufacture or otherwise specify an orthodonticappliance that applies a target force based on patient, tooth and casespecific factors so as to achieve optimal orthodontic treatment. Thehealthcare professional may for example use the point of care subsystemto input patient data including for example treatment requirements (suchas parameters describing the required movement of each tooth of thepatient) as well as other required patient data and to receive as outputa treatment plan and/or orthodontic appliance configuration parametersfor orthodontic treatment that is optimal for the specific patient underconsideration.

The orthodontic treatment system (1) may thus provide a distributedcomputing architecture for using optimal orthodontic forces to producean optimized appliance design and treatment plan. The computingarchitecture can include a device for measuring the PDL behavior in-vivoand the resulting measurements can be saved to a storage means or aserver for later use. The raw data can be processed by a computer systemand can be analyzed to determine for example a critical orthodonticforce, and a relating optimal orthodontic force. The system can alsoinclude a means for consuming the measured PDL behavior data to developmathematical models such as Artificial Intelligence (AI) or MachineLearning (ML) models describing the behavior of the PDL or the relatingtooth movement resulting from the application of a force to one or moreteeth. Such AI/ML models can be developed once or repeatedly and can beupdated to include newly measured or estimated data describing the PDLbehavior. The system can further include a means for communicating withexternal services such as a computer system or software process, and canreceive data from such services and return data to such services. Forexample, the system can receive the planned tooth movement fromtreatment planning software of a point of care system, and a ML modelcan use such planned movement as an input and return the resulting PDLbehavior curve for each tooth as an output. Said output can be receivedagain by a treatment planning software or can be received by anotherservice such as software to design an optimal orthodontic appliance. ThePDL behavior curve can be used to optimize the features of anorthodontic appliance, such as the appliance geometry, material,features and parameters defining such features, local placement,orientation, size or shape.

Aspects of the orthodontic treatment system (1) are now described ingreater detail below.

Measurement Subsystem and Associated Theory

Aspects of the present disclosure provide a measurement device andassociated systems and methods. In particular, aspects of the presentdisclosure provide a device, system and method for measuring data pointsand determining a critical orthodontic force using the measured datapoints. Aspects of the present disclosure may find application indetermining and defining the optimal forces for use in the correctivetreatment of malocclusion and other dentofacial defects using anorthodontic appliance (e.g., orthodontic aligners or dental braces). Theoptimal force may be a target orthodontic force based on (or a functionof) the critical force.

The measurement device and associated systems and methods describedherein may be configured for measuring and quantifying the biomechanicaltissue response and relating behavior of the PDL and surrounding tissueand determining a case-specific critical orthodontic force or otherdeterminate point (in some cases in-vivo or at least using measurementdata obtained in-vivo). The device may for example include a positionsensor and a force sensor configured to measure the tissue response ofthe PDL of a tooth when subjected to an applied force. The device mayinclude a user-feedback mechanism (e.g., in the form of a userinterface) to provide the user with a reading of the determined criticalforce and/or associated data (such as measurement data). The measurementdevice, system and method described herein may enable the measurement ofdata points for plotting a curve defining or quantifying the behavior ofthe PDL. Such a curve may generally represent the behavior of the PDL inresponse to certain, measurable, stimuli and may therefore generally betermed herein a “behavior curve”, or, in some instances morespecifically, a “non-pathologic tooth mobility curve” or a“force-displacement curve”. The data points measured herein may be usedto define a case-specific critical force level and may be referred togenerally as PDL behavior data or measured PDL behavior data.

The measurement device may be a handheld device and is configured forattachment to a part (typically tooth or bone) of a human or animalsubject. Once attached a user can impart forces onto the part to whichthe device is attached by urging the device towards or away from (orgenerally relative to) the subject. This handheld configuration mayprovide for a technologically simpler (and hence more cost effective),and in some cases a more convenient or user- and/or subject-friendly,device. The handheld configuration may be provided by shaping anddimensioning the or part of the body of the device so as to fit withinthe hand of the user.

FIG. 1B is a schematic diagram which illustrates an example measurementsystem (10) according to aspects of the present disclosure. In theillustrated embodiment, the measurement system (10) includes ameasurement device (102) connected to a computing device (104) via acommunication channel (106). The communication channel may be providedby a wired or wireless connection between the computing device and themeasurement device. The communication channel may be provided by anetwork, such as a local area network or a wide area network (e.g.,including a publicly accessible communication network, such as theInternet). In the example embodiment illustrated in FIG. 1B, thecomputing device (104) is a laptop computer although in otherimplementations the computing device may take on other forms, such as atablet computer, mobile phone (or smartphone), personal digitalassistant, desktop computer or the like. In other embodiments, themeasurement device may be a stand-alone device that operates withoutconnection to an external device but which, for example, communicateswith a data processing system (3) via a communication network (21).

The measurement device (102) includes a body (110) and an attachmentmechanism (112) fixed to and extending from the body.

The body (110) is configured for gripping by a user. In the illustratedembodiment, the body is configured for gripping by a user in that it isshaped and dimensioned to fit within the hand of a user. For example,the body may have an elongate and generally cylindrical shape. In someembodiments, the body is ergonomically shaped for example includingcontours that are shaped and dimensioned to accommodate individualfingers and a thumb of a user and/or the shape of a user's closed hand.The body may be shaped as a handle and may include non-slip formationsor features, such as knurled surfaces, rubberized surfaces or the like.In other embodiments, a part of the body, such as a handle, isconfigured for gripping by a user while another part is not soconfigured.

The attachment mechanism (112) is configured for attachment to a part ofa human or animal subject, in this example embodiment being a tooth (2).The attachment mechanism provides a means for creating an attachment toa tooth to transfer a stimulus or force in multiple directions. Theattachment mechanism includes a shaft (114) terminating in an attachmentformation configured for attachment to the intended part of the human oranimal subject (in this case being a tooth). The shaft is attached tothe body such that a force applied to the body is transferred via theshaft to the attachment formation and, in use, to the part of the humanor animal to which the attachment formation is attached. In someembodiments, the shaft is rigid and may be rigidly attached to the body.In some embodiments, the attachment of the attachment mechanism to thebody of the measurement device may be by way of a shock absorbing ordamping mechanism. The mechanism may be interposed between the shaft(114) and the body (110) so as to absorb or damp sudden movement appliedto the tooth via the attachment mechanism and body of the measurementdevice. The force sensor module (120) may be located on a proximal endof the shaft (i.e., the end opposite that to which the attachmentformation is fixed) and may be connected to the body via the shockabsorbing or damping mechanism such that force is transferred from thebody to the shock absorbing or damping mechanism and then to the shaftvia the force sensor module. This arrangement may improve subjectcomfort and may also improve the quality of the measurements as theforce applied to the tooth (and measured by the sensor module) will besmoother by virtue of the shock absorbing or damping mechanism.

In the embodiment illustrated in FIG. 1B, the attachment formationincludes a clamp (117) secured to the shaft and configured to clamp ontothe tooth.

FIGS. 2A and 2B illustrate two further example embodiments of anattachment formation (116A, 116B) according to aspects of the presentdisclosure. In the example embodiment of FIG. 2A, the attachmentformation (116A) includes a head (117A) connected to a cap (117B) thatfits onto the tooth. The cap provides a clamping mechanism that attachesto and clamps onto the tooth of a patient. The cap may be releasablyfixed in place using a suitable bonding agent (118). The cap may forexample be a silicone cap which can be removably secured to the tooth orto a plurality of teeth. In the example embodiment of FIG. 2B, theattachment formation (116B) includes a bracket connection formation(119A) configured to connect to a bracket (119B) that is releasablyfixed to the tooth. The bracket may be fixed to the tooth using, forexample, a bonding agent, UV curable resin or other suitable dentalmaterials for releasably securing the bracket to the tooth. The bracketincludes a clip, flanged protuberance or other suitable connectionformation which is configured to cooperate with a correspondingreceiving formation of the bracket connection formation. The receivingformation may for example be in the form of a claw, clasp or otherformation configured to receive and hold captive the connectionformation of the bracket. Any other form of mechanical mechanism can beused to create a temporary fixture to the tooth or for the user to holdon to the target tooth and apply a force and moment. In otherembodiments, the attachment mechanism may be configured for attachmentto a group of teeth (e.g., to adjacent incisors, molars or the like), inwhich embodiments the force and position measurements obtained by themeasurement device relate to the group of teeth and not to individualteeth.

The body includes or houses one or more of a force sensor module (120),position sensor module (122), a memory module (124), one or both of agyroscope (125A) and an accelerometer (125B), an external communicationinterface (126) and a processor (128) which may be interconnected via asuitable internal communication system, interface or bus(es) (I2C, SPI,etc.). The measurement device (102) includes or is in communication witha user interface (130). The gyroscope (125A) and accelerometer (125B)may be included in the body and/or the reference jig (e.g., asillustrated in FIG. 3C) and may be configured to determine theacceleration and rotation and/or orientation of the device,respectively, and the corresponding direction of the force. Thegyroscope (125A) is configured to output rotation and/or orientationmeasurement data and the accelerometer (125B) is configured to outputacceleration measurement data. In the illustrated embodiment, the memorymodule is shown as being a part of the measurement device. In otherembodiments, the memory module may be provided by a remote orcloud-based storage. For example, in some embodiments, the memory modulemay be provided by the datastore (7).

The force sensor module is configured to measure a force applied to theattachment mechanism, in use, and output force measurement data relatingto the measured force. The force sensor module measures the forceapplied to the attachment formation, and in turn the part of the humanor animal, via the shaft and body. The force sensor module may includeone or more commercially available sensors that allow a force to bemeasured along at least one dimension. In some cases, the force sensormodule may include an integrated six degrees of freedom force sensor. Insome embodiments, the force sensor module may include a custom forcesensor or force transducer (e.g., in the form of one or more load cells)configured to measure data points relating to a force applied by theuser. The force sensor module may output force measurement values foreach of six degrees of freedom, for example a translation value and arotation value along or about each of the x-, y-, and z-axis.

The position sensor module is configured to measure the position of thepart of the human or animal relative to other parts thereof, in use, andoutput position measurement data. The position sensor module may includeone or more of a camera, accelerometer, gyroscope, magnetometer, Halleffect sensor or the like. In the example embodiment illustrated in FIG.1B, the position sensor includes a camera and computer vision systemconfigured to locate position of tracking points (132) in image data tocalculate the relative change in position, and hence to determinemovement or displacement, according to the biomechanical response. Thetracking points may be existing, distinguishable points that thecomputer vision system identifies automatically, may be drawn directlyonto the target tooth (2) and surrounding teeth or may be provided on anattachment formation (such as a head, cap, clamp bracket connectionformation) and a reference jig (134) that is fixed to the teeth. Theposition measurement data may be used to calculate or determine toothdisplacement, for example by tracking a series of different toothpositions over a period of time, and to output tooth displacement ormovement data. It should be appreciated that the position measurementmodule described herein is configured to measure position of a targettooth relative to other teeth, which is different from measuring changesin position between the target tooth and the body of device. This mayenable a decoupling of device movement from tooth movement which mayimprove accuracy of the position measurements given the handheld natureof the device (and there may hence be no fixed reference point).

The force measurement data and position measurement data maycollectively be referred to herein as biomechanical response data.

An example reference jig (134) setup is illustrated more clearly in FIG.3A. The reference jig is configured for attachment to a plurality ofteeth which are proximate or adjacent the target tooth (2). Thereference jig can include several tracking points. These tracking pointsare suitable for visual identification and location using a computervision system. The reference jig is placed into the mouth in such a waythat it remains unaffected by a stimulus being applied to the targettooth and thus does not move. FIG. 3B illustrates an example of thefield of view of the camera and computer vision system used to measurethe biomechanical tissue response. The camera is configured in such away that it is able to capture image data (136) including the trackingpoints on the reference jig as well as the tracking points on the targettooth. Sufficient tracking points are located on the reference jig andthe target tooth for the computer vision system to be able to calculatethe relative movement or displacement of the target tooth relative tothe reference jig with six degrees of freedom including translation androtation around all three axes. The camera and/or computer vision systemmay therefore be configured for use in capturing image data includingreference points on a target tooth and reference points on the one ormore other teeth and allowing measurement of the biomechanical tissueresponse.

Referring now to FIG. 3C, in some embodiments the reference jig (134)includes an accelerometer and/or gyroscope (135) which may transmitacceleration measurement data and rotation and/or orientationmeasurement data to the processor of the measurement device. Theprocessor can process the reference jig accelerometer/gyroscope datatogether with the measurement device accelerometer/gyroscope data todetermine components of each of the six degrees of freedom of the forcemeasurement data and the position measurement data. The accelerometersand/or gyroscopes provided in the body of the device and/or thereference jig enable the determination or calculation by the processorof components of each force measurement data point for each of sixdegrees of freedom. The accelerometer or gyroscope measurement data maybe used to determine the relative direction of application of a force,thereby allowing the orientation and each of the six degrees of freedomof the measured force data and the measured displacement data to bedetermined.

In another example embodiment, the position sensor module includes amagnetic sensor system (e.g., including one or more magnetic sensors,such as Hall effect sensors). The magnetic sensors may be housed withina reference jig and in data communication with the measurement device(e.g., via the external communication interface). A magnet may be fixedto the target tooth (or, e.g., housed within an attachment formation ofthe device). The reference jig is attached to other teeth or part of thedental complex in such a way that is not affected by the stimulus beingapplied to the target tooth. If a force is applied to the target tooth,this will cause the tooth to move relative to the reference jig. Thisrelative displacement will be sensed by the magnetic sensor system.Sensor data from the magnetic sensor system may be transmitted to thedevice for processing to determine position measurement data. In thisembodiment, the device uses a magnetic sensor system to measure thetissue response. The system makes use of a magnetic sensor integratedinto a reference jig and a magnet that is attached to the tooth to whicha stimulus is being applied.

The memory module is configured to store measurement data, including theforce measurement data and position measurement data, displacementmeasurement data and optionally rotation and/or orientation and/oracceleration measurement data as well. The data may be stored inassociation with the target tooth (e.g., by way of tooth type and/ortooth position indicator) and a human/animal subject identifier forlinking the measurement data to metadata associated with thehuman/animal subject. In the embodiment illustrated in FIG. 1B, thememory is illustrated as being internal to the device. In otherembodiments, the memory in which measurement data is stored may beprovided by a remote device (such as a computing device).

The processor may be configured to manage and/or control the variousmodules of the measurement device. The processor may be configured toreceive measurement data from the sensor modules, process, timestamp,filter, output and/or store the measurement data received from thevarious sensor modules. The processor may be configured to processposition measurement data to determine displacement measurement data.The processor may for example store the measurement data (and associateddata, such as timestamps) in the memory module. The processor may beconfigured to output measurement data, reference data and/or target datato the user interface. The processor may further be configured totransmit the measurement data and/or associated metadata to thecomputing device via the communication channel (106). The processor mayimplement a feedback system using one or more of the force sensormodule, position sensor module and user interface.

The user interface may be configured to output to the user one or bothof the force measurement data and the position measurement data. In someembodiments, the user interface is configured to output to the user oneor both of force reference data and position reference data. In someembodiments, the user interface is configured to output to the user oneor both of force target data and position target data. In someembodiments, the user interface is configured to display the forcemeasurement data and force reference data. The force measurement dataand force reference data may for example be overlaid or otherwise showntogether to facilitate matching by the user, in use through manipulationof the body, of the force measurement data to the force reference data.The user interface may therefore be configured to guide application offorce by the user. The user interface may include an input element(e.g., provided by way of keyboard, buttons, touch-sensitive display)for input of information such as a patient identifier, tooth (or bodypart identifier) or the like.

In the embodiment illustrated in FIG. 1B, the measurement deviceincludes a user interface (130) integrated into the body. The userinterface may for example be provided by one or more LEDs, a liquidcrystal (or other suitable) display or the like. In some embodiments,the measurement device is in communication with a user interface, whichmay for example be provided by the computing device (104).

FIG. 4A illustrates one example embodiment of a user interface providedby a computing device, for example in the form of a tablet or laptopcomputer. The user interface may include one or more windows includinggraphical representations of measurement data, reference data and/ortarget data. One example window includes a gauge (140) which overlays agraphical representation of force measurement data (142) with agraphical representation of force reference data (144) so as to guidethe user as to the actual force he or she is applying to the toothversus the desired or required force for plotting of a behavior curve. Amagnitude of the force measurement may be displayed (145) in the centerof the gauge.

Another example window includes a one or more arrows (146), where eacharrow is a graphical representation of force measurement and/orreference data and can have varying properties including for examplelength, size, or color, such properties being varied based on thedesired and the measured stimulus and being adjusted so as to providethe user with instructions on how to change the applied stimulus to thetarget tooth.

Another example window includes a graphical representation of thereference and/or the measured force in three-dimensional space. Thesignals can be superimposed on image data (148) or a three-dimensionalrendering of the one or more teeth. The same visualization can also beused to visualize the reference (e.g. target or desired) force (150) andthe measured force (152), and may also include a visualization of areference moment (154) and a measurement moment (156). The displayedsignal can include any properties describing the force with six degreesof freedom, each having one or more of a direction, a magnitude, aduration, and frequency of application. Another example window includesa graphical representation of force measurement and reference data byway of a time series visualization (158) of the desired reference (159A)and the measured (159B) forces in real-time. The reference signal canhave at least a direction and a magnitude, as well as time dependentcharacteristics including but not limited to frequency, rate of change,duration of application and the like.

One or more of these windows may be provided by way of the userinterface so as to provide feedback and instructions to the user. Theuser interface can include information about the desired referenceforces to be applied to the target tooth as well as the actualmeasurement of forces, which can be displayed in real time.

FIG. 4B illustrates one example embodiment of a user interface providedby a computing device, for example in the form of a mobile phone. Theuser interface may include one or more windows including graphicalrepresentations of measurement data, reference data and/or target data.One example window includes a three-dimensional visualization (160) ofthe teeth and the target tooth and a superimposition of the graphicalrepresentations of the measured (162) and reference (164) force data.The forces can be visualized in real-time. The representation of theforces can include the magnitude, direction and other parametersdescribing the desired or the applied stimulus. The user interface canvary the parameters of the signal including the size, orientation,length or color to be indicative of the displayed signal.

The user interface may be configured for input of one or both of a toothtype indicator and tooth position indicator associated with each of theteeth and the human or animal subject from which measurements are to beor have been taken. The user interface may be configured for input ofmetadata associated with the human or animal subject from whichmeasurements are to be or have been taken. The tooth type indicator andtooth position indicator may be stored in the memory module inassociation with the measurement data. The metadata may be stored in thememory module in association with a human/animal subject identifier(e.g., human ID) for linking the metadata to the measurement data.

In another example window the user interface can provide instructions tothe user on how to adjust the current applied stimulus so as to betterrepresent the desired stimulus. Such a visualization can make use of oneor more arrows (166), each of which can have properties including thesize, color, length and orientation, which can be changed so as toprompt the user to apply a stimulus which better represents the desiredstimulus. Said properties can be changed based at least to some extenton one or more of the desired stimulus and the measured stimulus. Amagnitude of the force measurement may be displayed (168) in the centerof the base from which the one or more arrows extend.

The system and device described above may be used for the measurement,storage and transmission of force and position data, for example asapplied to and measured from a part of a human or animal subject. Theforce and position measurement data may be used to plot a behavior curverelating to the relevant part of the human or animal subject. While thesystem and device of FIG. 1B are described with reference to an exampleillustration in which the device is connected to computer via a wiredconnection, it should be appreciated that in other implementations thedevice can also operate in stand-along mode without a connection to anyexternal device, or can be connected to a computer, a phone, tablet orany other suitable computing device in this way. The connection can bein the form of a USB or other connection and can provide both datacommunication and or power supply to the device. The device can alsoconnect to any other electronic device or interface using any form ofwireless communication including for example Wi-Fi, Bluetooth or thelike.

The measurement device described above utilizes energy from a human useror operator to impart forces onto the tooth or teeth of the human oranimal subject. It should however be appreciated that other embodimentsanticipate other mechanisms for imparting such forces. For example, someembodiments of a measurement device according to aspects of the presentdisclosure may include a hydraulic or piezoelectric actuatorcontrollable by the user so as to apply a desired force. Measurementdevices according to aspects of the present disclosure therefore enablea user-controllable force to be imparted on or applied to a tooth orteeth of a human or animal subject for the purpose of measuring acorresponding displacement of that teeth or those teeth. The arrangementof the measurement device (i.e., user controllable force and decoupleddisplacement/position measurement features) may reduce noise in themeasurement data collected by the device.

The measurement system and/or device may implement a method formeasuring data points relating to force applied to and/or position of apart of a human or animal subject relative to other parts of the humanor animal subject. FIG. 5 is a flow diagram which illustrates an exampleof such a method. The method may be conducted by a measurement device(102), such as that described in the foregoing.

The method may include receiving (201) an instruction to commencemeasuring. In some embodiments, this may include receiving a user inputinstruction, for example an instruction from the user to startmeasuring, an instruction indicating that the device has been attachedto a part of a human or animal subject, or the like. The user input canbe input into the user interface (e.g., by clicking or selecting“Proceed with measurements”, “Ready to measure”, “Attachmentsuccessful”, “Start” or the like). In other embodiments, receiving theinstruction may include receiving an auto-generated (i.e., devicegenerated) instruction which may for example be generated by the devicein response to the device detecting attachment of the attachmentmechanism to a part of the human or animal subject.

Receiving the instruction to commence measuring may thus includereceiving or otherwise be associated with an indication of attachment ofan attachment mechanism of the measurement device to a part of a humanor animal subject. As described in the foregoing, the attachmentmechanism is fixed to and extends from a body of the device which isconfigured for gripping by a user.

In response to receiving the instruction, the device may start samplingdata points. This may include instructing the appropriate sensor modulesto start sampling data points; receiving, processing and/or storing datapoints and the like. Receiving the instruction may thus trigger thesampling of data points.

The method includes receiving (202) force measurement data and receiving(204) position measurement data. The force measurement data may bereceived from a force sensor module and the position measurement datamay be received from a position sensor module. Each of the forcemeasurement data and position measurement data may include a series ofdata points relating to discrete force and position measurements,respectively, obtained at specific points in time. Each set of datatherefore describes changes in an applied force and changes in aposition of the part of the human or animal subject relative to otherparts, respectively, over a period of time. The modules from which thedata is received may be configured with a high sampling frequency, suchthat a number of data points are measured per second, for example. Thesensors may for example obtain measurement samples at anywhere between10 Hz and 50 kHz. The processor can apply filtering, averaging orsmoothing to the sample points.

The received data may be timestamped (e.g., by the respective modules bywhich they are measured) and/or the method may include timestamping(206) the force measurement data and position measurement data as it isreceived. Timestamping may include timestamping filtered, averaged orsmoothed sample points. Timestamping operates to relate a given forcemeasurement data point to a corresponding position measurement datapoint so that a plot of the relationship between force and displacementcan be generated (e.g., as shown in FIGS. 19A-19C).

The method includes storing (208) and/or transmitting (210) measurementdata, including the received and timestamped force measurement data andposition measurement data. Storing or transmitting the received andtimestamped force measurement data and position measurement dataincludes storing or transmitting force measurement data points andcorresponding displacement measurement data points having been measuredin-vivo while applying a force to a particular tooth of a human oranimal subject. The data may be stored in the memory module for offlineretrieval (e.g., for uploading at a later point) or may be transmittedto the computing device or a remote server. Storing the measurement datamay for example include storing the measurement data in a commaseparated value (CSV) or appropriate format. Stored measurement data mayfor example take on the form:

yyyymmddhhmmssms, Ftx, Fty, Ftz, Dtx, Dty, Dtz, Fmx, Fmy, Fmz, Dmx, Dmy,Dmz, , , ,20200412084530900, 12.30, 2.10, 32.65, 11.34, 18.10, 9.50, 128.20,90.40, 23.43, 78.23, −12.30, −98.20, , , ,20200412084530905, 13.00, 2.30, 33.98, 11.90, 17.50, 9.50, 137.80,−95.60, −20.98, 83.45, −5.40, −98.41, , , ,20200412084530910, 13.70, 1.20, 35.31, 12.10, 17.95, 9.56, 143.20,−98.70, −18.87, 89.67, −0.23, −98.98, , , ,20200412084530915, 14.40, 1.80, 36.64, 12.54, 17.70, 9.58, 151.40,−103.20, −16.53, 95.22, 6.09, −99.31, , , ,20200412084530920, 15.10, 1.35, 37.97, 12.92, 17.63, 9.61, 158.90,−107.35, −14.25, 100.94, 12.13, −99.70, , , ,20200412084530925, 15.80, 1.15, 39.30, 13.30, 17.55, 9.64, 166.40,−111.50, −11.97, 106.66, 18.16, −100.09, , , ,20200412084530930, 16.50, 0.95, 40.63, 13.68, 17.48, 9.67, 173.90,−115.65, −9.69, 112.38, 24.20, −100.48, , , ,20200412084530935, 17.20, 0.75, 41.96, 14.06, 17.40, 9.70, 181.40,−119.80, −7.41, 118.10, 30.23, −100.87, , , ,20200412084530940, 17.90, 0.55, 43.29, 14.44, 17.33, 9.73, 188.90,−123.95, −5.13, 123.82, 36.27, −101.26, , , ,20200412084530945, 18.60, 0.35, 44.62, 14.82, 17.25, 9.76, 196.40,−128.10, −2.85, 129.54, 42.30, −101.65, , , ,20200412084530950, 19.30, 0.15, 45.95, 15.20, 17.18, 9.79, 203.90,−132.25, −0.57, 135.26, 48.34, −102.04, , , ,20200412084530955, 20.00, −0.05, 47.28, 15.58, 17.10, 9.82, 211.40,−136.40, 1.71, 140.98, 54.37, −102.43, , , ,20200412084530960, 20.70, −0.25, 48.61, 15.96, 17.03, 9.85, 218.90,−140.55, 3.99, 146.70, 60.41, −102.82, , , ,20200412084530965, 21.40, −0.45, 49.94, 16.34, 16.95, 9.88, 226.40,−144.70, 6.27, 152.42, 66.44, −103.21, , , ,

The method may include outputting (212) the measurement data (e.g. oneor both of position and force measurement data) to a user interface.Outputting the data may include outputting reference data (e.g. one orboth of position and force reference data) to the user interface. Themeasurement data and reference data may be overlaid or juxtaposed orotherwise displayed so as to guide the user as to the application offorce. Example arrangements of measurement and reference data output toa user interface are illustrated in FIGS. 4A and 4B. The method repeats(214) for continual receiving, timestamping, storing, transmittingand/or outputting of measurement data.

Aspects of the present disclosure are directed specifically to themeasurement of data points relating to force applied to, and positionof, a target tooth of a human subject relative to other teeth of thehuman subject. The measurement data is obtained for the purpose ofplotting a behavior curve that describes the relationship betweenchanges in forces applied to the target tooth and changes in position ofthe target tooth relative to the other teeth. Such a curve may be usefulin determining an optimal force for use in the corrective treatment ofmalocclusion and other dentofacial defects using an orthodonticappliance or aligners. More specifically, an optimal force that ispatient- (or subject-) specific, tooth specific and, in some cases,stage of treatment regimen specific may be determined.

The measurement data may be obtained using a measurement device that hasno internal mechanism for applying a force. Instead, the measurementdevice may be configured to transfer a force that is applied to thedevice by a human user onto a target tooth to which the device isattached. In order to guide the human user as to the appropriate forceto apply, the device may include a user feedback system configured tooutput measurement data to the human user via an appropriate userinterface.

FIG. 6 is a schematic diagram which illustrates an example embodiment ofa feedback system implemented by the measurement system and devicedescribed herein. The processor (128) is configured in such a way as toprovide feedback (250) to the user (252) via a user interface with anindication that the user can apply a stimulus such as a force to thetarget tooth. The user feedback can include instructions to the userregarding what stimulus to apply, the magnitude of force as well as thedynamic or movement applied to the tooth. In addition, the device cannotify the user in case an incorrect stimulus is being applied. In itssimplest form the user interface can be minimalistic and use onlyindicator LEDs. In a more advanced version of the UI the user can seethe measured stimulus and resulting biomechanical tissue response inreal-time as well as real-time analytics showing parameters relating tothe behavior of the biomechanical tissue response (e.g. as illustratedin FIGS. 4A and 4B). This step can make use of the indicator LEDs, ascreen on the device or a user interface on a computer. The user holdsthe device and applies (254) a stimulus to the target tooth to which thedevice is connected via the shaft and attachment. The device forcesensor module (120) is configured to measure the stimulus applied by theuser in real-time and to provide the measured stimulus back to themicroprocessor in the form of one or more data points. The signalreceived by the processor can be processed and the data received can beused to determine a new signal or user feedback provided to the user. Inaddition, the position sensor module is configured in such a way as tomeasure the biomechanical tissue response (256) in real-time. Thisposition sensor module (122) is also configured in such a way as toprovide one or more data points back to the processor. The processor canmake use of the data received describing the applied stimulus (forcemeasurement data), the data received describing the biomechanical tissueresponse (position measurement data) or a combination of the two todetermine the user feedback and signal. The device can make use of auser interface to display the data in any form and in real-time.

Referring now to FIG. 7, in one possible use case, the device (102) isattached (302) to a target tooth using an attachment mechanism to thetooth. The user of the device then applies (304) a stimulus in the formof a forward (pushing) force to the tooth, which is followed by anapplication (306) of a force in the opposite direction (pulling force).The device measures (308) the applied stimulus in real-time as well asthe resulting biomechanical tissue response in real time (e.g. asdescribed above with reference to FIG. 5). The device compares (310) themeasured biomechanical tissue response to previous cycles. The deviceassesses (312) the change in the biomechanical tissue response anddepending on the assessment, provides the user with an indication ofwhat stimulus to apply. Typically, the assessment will include changesin the displacement, the gradient (or “ease of movement”), for exampleas described in FIGS. 20-25. This process can be repeated (314) for thesame tooth, or on another target tooth. Repeating (314) the process mayinclude recommencing the process at applying (304) a forward force or atattachment (302) of the device and sensors. In other words, the processmay be repeated for the same tooth or for another tooth.

In cases where a cyclic stimulus is applied to one or more teeth in aback and forth motion (e.g. as in the example use-case described abovewith reference to FIG. 7), the neutral position of the tooth may need tobe identified from the raw data. FIG. 8 is a flow diagram whichillustrates an example method for determining the neutral position of atooth in biomechanical response data. The method may be conducted by ameasurement device and/or computing device. The method includes theidentification of the position through which the tooth moves at whichthe movement occurs most easily, or in other words the change indisplacement per unit of force is highest. The force level at which thisoccurs, can be used to center the raw data of the cyclic movementmeasurement. The method includes obtaining (320) the raw measurementdata (e.g. raw force and position measurement data) and calculating(322) the average displacement curve as a function of the appliedstimulus. The method includes calculating (324) the derivative of thedisplacement with respect to the applied force. The method includescalculating (326) the second order derivative of the measuredforce-displacement curve. The method includes identifying (328) thepoint at which this curve is equal to zero, which indicates the forceand position at which the tooth is at its neutral position which areselected or identified (330) as an origin around which to center thebiomechanical tissue response curve. An example graph including anaverage displacement curve and derivative curve is illustrated in FIG.25 and is indicative of the “ease of movement”. In other words, thepoint at which this curve is highest is likely to coincide with theposition at which the displacement of the tooth is zero and likewise thecompression of tension of the surrounding biomechanical tissue wouldlikely be at its lowest.

In previous examples the user applied a stimulus to the target tooththat followed a cyclic movement and possibly a back and forth or pushingand pulling movement. FIG. 9 is a flow diagram which illustrates anotherpossible use case of the device (102) in which the user can apply astimulus of random magnitude and random direction. The first stepincludes attaching (340) the reference jig (if applicable) to the one ormore teeth and attaching (342) the attachment mechanism to the targettooth. The user then applies (344) a random force that can vary inmagnitude, direction, and frequency and can include six degrees offreedom movement. The force sensor module on the device is configured tomeasure (345) said randomly applied stimulus and can assign a timestampto the value measured at a specific time. Simultaneously, the positionsensor module measures (346) the resulting biomechanical tissue response(position measurement data) in real-time and can assign a timestamp toeach of the measurement values. In a next step the processor of thedevice can receive the measurement data from each of the modules and canuse the measurement data point at each given timestamp to calculate(348) the behavior curve (or force-displacement curve and the behaviorof the PDL resulting from a randomly applied stimulus).

FIG. 10 is a flow diagram which illustrates and example feedback methodaccording to aspects of the present disclosure. The method may beconducted by a measurement device. The feedback method is a process byway of which the user is presented with reference data for the stimuluswhich should be applied to the target tooth. The feedback may be inreal-time and may be based on measurement data (including e.g. ameasured biomechanical tissue response) and hence the measured behaviorof the PDL. As with previous embodiments, the device and reference jig(if applicable) are attached to the one or more teeth and the tip of thedevice is attached to the target tooth. The device then provides (360)the user with a signal that indicates that the user can apply astimulus. This may include outputting reference data (e.g. forcereference data) indicating the magnitude, direction and or moments offorce that the user is to apply by moving the measurement devicerelative to the tooth to which it is attached. This feedback can beprovided to the user in a number of ways including but not limited toindicator LED's, a screen or user interface on the device or a userinterface on an external device (e.g. as described above with referenceto FIG. 4A or 4B). In response, the user applies a stimulus according tothe reference signal that was provided to the user by the device. Thedevice measures (362) the applied stimulus (force measurement data) andmeasures (364) the resulting biomechanical tissue response (positionmeasurement data), and calculates (366) the resulting behavior curve (orforce-displacement curve). Based on the analysis of the sensormeasurement data, the device determines (368) updated reference data andcan provide the updated reference data (e.g. as feedback) to the user.This process can be repeated, thereby essentially creating a closed-loopfeedback loop and control system that allows a controlled stimulus to beapplied to the one or more target teeth.

Aspects of the present disclosure relate to obtaining force measurementdata and position measurement data (which together may be termedbiomechanical response data) for the purpose of determining one or moredeterminant points, a target orthodontic force, a stage force value,stage movement value and the like. The data points measured herein mayfor example be used to define a case-specific critical force level orother appropriate determent point. An optimal force may be a function ofthe critical force.

FIG. 11 is a flow diagram which illustrates an example method fordetermining or calculating a critical force according to aspects of thepresent disclosure. The flow diagram illustrates one possible processfor repeatedly defining determinate points and force levels based on themeasured biomechanical tissue response. More specifically, this processallows the definition of a patient, tooth and case specific value of acritical force level Fc to be determined.

The method may be conducted by a measurement device or a computingdevice. The method includes obtaining (380) raw data describing thebiomechanical tissue response. This may include receiving measurementdata from the measurement modules, retrieving the raw data from thememory module or a computing device receiving the raw data from themeasurement device via the communication channel. The method includescalculating (382) the average force-displacement curve (or behaviorcurve). The method includes applying (384) segmented linear regressionto the behavior curve to classify the different regions or stages ofnon-pathologic tooth movement. An example illustration of this processis also shown in FIG. 24. The segmented linear regression includes (385)fitting straight lines to the behavior curve that describe the initialtooth movement (ITM) and secondary tooth movement (STM) stages of toothmovement resulting from an applied stimulus. When a cyclic movement isapplied, the intersection of the ITM stages in opposite directions isfixed at the origin. The method includes determining (386) intersectionof the ITM and STM stages in both the forward and backward direction.The method includes identifying (388) the critical force (Fc) as beingthe force magnitude at the intersection of the ITM and STM. It is atthis point that the PDL is partially compressed and this determinatepoint can therefore be used to identify a critical force level Fc. Themethod may include storing this value (Fc) in a database in associationwith one or more of: a patient identifier; tooth identifier; dateinformation; patient health information; treatment regimen information;the raw force measurement data; the raw position measurement data, andthe like. The method described above with reference to FIG. 11 includesvarious steps to define a determinate point on the curve describing thePDL behavior using segmented linear regression.

The measurement systems, methods and devices described herein relate tothe measurement of data points and determination of a criticalorthodontic force (or simply critical force) using the measured datapoints. In the description that follows, an overview of the underlyingmedical and scientific theory is presented together with an explanationof the effects that the various forces applied to target teeth may haveon the orofacial structures as well as various outputs that may beobtained using the systems and methods described herein.

FIG. 12 shows a possible tooth displacement resulting from an appliedforce F. The force F is applied in such a direction that the force doesnot directly pass through the center of resistance (C_(RES)) of thetooth, and thus is expected that the resulting movement will includeboth a translation as well as a rotational component. These conceptshave been studied extensively and are well understood in the field oforthodontics, but in general a force of certain direction and magnitudewill result in two components, a translational and a rotationalcomponent. The translational component is equal to the direction andmagnitude of the force, while the rotational component or resultingmoment is a function of the force direction and magnitude as well as theperpendicular distance of the force away from the Centre of Resistanceof a tooth. The Figure shows one scenario where the force F is appliedat a perpendicular distance away from the Centre of Resistance. Theresulting movement of the tooth within the alveolus consists of both atranslation and rotation. The overall movement can be described as apure rotation about the Centre of Rotation (402), which in this casedoes not coincide with the Centre of Resistance.

The Centre of Resistance (Cres) is a function of the tooth and rootshape and morphology, the thickness and the shape of the PDL and otherrelated factors. A shorter tooth root will cause Cres to lie morecoronally toward the crown of a tooth, while a longer tooth root willresult in Cres lying more apically toward the tip of the tooth root.Similarly, any plurality of tooth roots of an individual tooth ordifferent shapes will affect the position of Cres. FIG. 13 shows thedifferent positions (401, 403, 404) for the Centre of Resistance ofdifferent teeth (405, 406, 407).

FIG. 14 illustrates the same force F being applied at different pointsand in different directions (A, B, C, D, E) on a tooth. Each force willresult in a different type of movement of the tooth. The translationalcomponent will be the same for all forces except for FE for which thedirection differs. The perpendicular distance of FA from Cres is largestfor FA. FA will thus have the largest moment applied to the tooth, whileFB will have a smaller and FC will have an even smaller moment. Theforce FD could practically not be applied at the Cres, but in theorywould result in a zero moment and only a translational force component.Similarly, the vector of FE passes through Cres and would thus have atranslational component only resulting in pure translation of the tooth.While it may not be possible to apply a force such as FD at the Cres ofthe tooth crown, a force couple can be applied at for example the samepoint as FB, consisting of a force FB as well as a moment in theopposite direction as the moment resulting from FB. The net forceapplied to the tooth would then be the same as FD. The current inventionis able to calculate the resulting stimulus applied to the one or moreteeth based on the force sensor measurement and the position of theapplied force in real-time and is able to provide feedback to the userbased thereon. In addition, the device is able to provide a referencesignal to the user in order to achieve a stimulus that best approximatesthe desired stimulus applied to the target tooth.

FIG. 15 (shown as FIG. 15(a) and FIG. 15(b)) is a side profile of atooth showing the differences that can occur in tooth root and bonemorphology. In FIG. 15(a), the PDL (410) shown has a uniform profile andthickness around the tooth (412). FIG. 15(b) shows an example of a tooththat has localized differences (410A, 410B) in PDL thickness due tovariations in the tooth root morphology and in the morphology of thealveolar bone (414) structure surrounding the tooth root. The differentPDL thicknesses would result in different biomechanical tissue responseswhen subject to the same stimulus. This difference in behavior of thePDL can be measured and quantified using the current invention. Alocalized change in shape of either the root or the bone can limitmovement of the PDL within the alveolar cavity, thus showing a smallerdisplacement for a first root or bone morphology as opposed to a largerdisplacement for a second, different, root or bone morphology resultingfrom the same applied stimulus. Similarly, differences in the tooth rootand alveolar bone morphology, can result to a different biomechanicaltissue response in the forward or the backward direction as shown inFIG. 23 where the displacement in the one direction e.g. a pushingforce, is larger than the displacement in the opposite direction e.g. apulling force.

FIG. 16, shown here in FIG. 16(a) and FIG. 16(b) is a side profile of atooth in a position before a stimulus has been applied to the tooth andafter a force (F) has been applied to the tooth. The stimulus or forceapplied to the tooth causes the PDL (410C) to become compressed as shownin FIG. 16(b). At a lower force, the compression of the PDL is limitedby the visco-elastic effects of the PDL which can be measured using thecurrent invention. At a lower force (F) the biomechanical tissueresponse as a function of the applied force increases faster, while at ahigher force (F) the response and displacement of the tooth is limitedby the alveolar bone, thus an increase in the applied force (F) does notlead to the same change in the measured tissue response.

FIG. 17 is a side profile of a tooth showing the points at which a force(F) can be applied and the different points Point A and Point B at whichthe biomechanical tissue response can be measured. The Figure also showsthe Centre of Rotation (Crot) (402) around which the tooth moves. Themovement of a tooth when a force is applied to the same tooth can beeither pure translation, or pure rotation, or a combination of the two.The movement can further be in all directions including 6 degrees offreedom. In the example shown in FIG. 17, the displacement and movementof the tooth around the Centre of Rotation will result in a higherrelative displacement of Point A than for Point B. The device canmeasure the biomechanical tissue response at either Point A or Pont B orany other point of the tooth or multiple points on the tooth. It isfurther possible to determine that the movement of Point A and Point Bboth describe the same biomechanical tissue response, where the one issimply a multiple of the other, assuming that the tooth is rigid or thedeformation thereof is negligible.

FIG. 18 is an example plot of the critical force as a function of timefor various different teeth, which illustrates the measuredbiomechanical tissue response and behavior of the PDL are indicative ofthe various factors relating to the alveolar complex and the boneremodeling process during orthodontic treatment including, but notlimited to tooth and root morphology, PDL thickness, health, and generalstate of the PDL, bone type, blood pressure, muscular activity, patientage and patient health. Since many of these factors change over time thebehavior of the PDL also varies over time and the current invention isable to measure and quantify the biomechanical tissue response todetermine this change over time. These changes are especially presentduring orthodontic treatment during which time significant changes occurto the applied forces and subsequent bone and tissue remodeling. FIG. 18shows an example of the critical force level Fc as one possibledeterminate point established at different times during treatment forthree different types of teeth. The plotted values of Fc over the courseof treatment change due to the changing tissue structures of the PDLduring treatment and this change over time can further be different foreach individual patient or tooth.

The measurement device described herein is able to measure the stimulusapplied by the user as well as the resulting biomechanical tissue(position data) response in real time. For example, the user could applya force to a tooth in a back and forth motion, alternating between apushing and a pulling force applied to the tooth. The device wouldreceive a measurement of the applied force at a specific point in time,and the device would also receive a measurement of the resulting toothdisplacement at a specific point in time. The processor could assign atimestamp to each of the measurements, allowing these to be receivedasynchronously. Data can be processed separately or sent to an externaldevice or processor and can be correlated again using the timestamp. Byrelating the measured stimulus to the resulting tooth displacement, thedevice would be able to determine the force-displacement curvedescribing the behavior of the periodontal ligament as shown in FIG.19A. The raw force-displacement data (416) shows a hysteresis effect dueto the visco-elastic effects of the periodontal ligament. The raw datacan be used to determine the average biomechanical tissue response for agiven stimulus. In the example given, the average force displacementcurve (418) can be calculated.

The measurement device described herein can measure the biomechanicaltissue response at different points on the one or more teeth to which astimulus is being applied. If for example the tooth displacement ismeasured, this can be different when measured at different points on thetooth and the tooth displacement can include both translation, orrotation, or a combination thereof. FIG. 20 shows an example of thebiomechanical tissue response measured at two different points, Points Aand Point B (e.g. corresponding to those points illustrated in FIG. 17)on the same tooth, for the case where the tooth movement can includeboth translation and in this case is rotated around the Centre ofRotation (402). The displacement measured at Point A will be greaterthan the displacement measured at Point B. More specifically, thedisplacement of Point A will be a multiple of the displacement of PointB or vice versa. Both curves and measurement at the two points describethe same biomechanical tissue response.

The force or stimulus applied to the one or more teeth can be appliedrepeatedly over time and the resulting biomechanical tissue response canbe measured. For example, the user can apply a repeated pushing andpulling force to one or more teeth. The measured biomechanical tissueresponse can vary for each cycle of the applied force, as illustrated inFIG. 21. For example, the displacement of a tooth can increase for eachcycle of an applied force due to the visco-elastic effects of the PDLand the biomechanical tissue. The applied pressure can lead to fluidsbeing pushed out of the tissue and lead to compression of thesurrounding alveolar bone. Due to the reduced viscous resistance andcompression of the surrounding tissue, a greater displacement can bemeasured for the same level of force that is applied for each subsequentcycle. The device can measure and monitor the change in thebiomechanical tissue response over time.

FIG. 22 shows another example of the change of the biomechanical tissueresponse when subject to repeated cyclic stimulus. In this example, theinitial response changes more than the tissue response at larger forces.This effect can be seen due to a change in the visco-elastic response ofthe PDL and surrounding tissue and specifically due to the change influid effects. Applying pressure to the PDL can lead to fluids beingremoved from the tissue, thus leading to a lower resistance onsubsequent loading cycles. This change is greater in the initial stageof displacement, where viscous effects are more prominent, and smallerin the secondary stage of tooth movement, where the elastic effects offor example bone compression are greater.

A force or stimulus can be applied to one or more teeth in any directionand in six degrees of freedom. Due to the different shape of the PDL andnon-uniform root and bone morphology, the biomechanical tissue responsecan be different in different directions, as shown in FIG. 23. Here apositive force leads to a larger measured displacement dx, while anegative force or the same magnitude leads to a lower relativedisplacement.

One example of biomechanical tissue response measured by the currentinvention can in general be divided into different stages ofnon-pathological tooth movement. These two stages can be described asthe initial tooth movement (ITM) stage and the secondary tooth movement(STM) stage. The ITM occurs at lower forces while the STM stage occursat greater force levels. One possible way of repeatedly determiningthese stages of tooth movement is using segmented linear regression, forwhich a straight line is fitted to the measured force-displacement datadescribing the biomechanical tissue response the result of which isshown in FIG. 24.

FIG. 25 illustrates another example, in which the measured biomechanicaltissue response can be analyzed to determine the direction of the tissueresponse as well as the magnitude of the tissue response as a functionof the applied stimulus. By taking the first and second derivatives ofthe tooth displacement with respect to the applied force, it is possibleto determine for example the ease of movement. This parameter, which isobtained by taking the second order derivative, could be indicative ofthe force range which would be safe to apply to a tooth or within whichno long-lasting negative effects could be expected.

It should be clear from the foregoing that the force magnitude is notthe only factor affecting the rate of tooth movement and that variousother factors exist that need to be taken into consideration andcontrolled to induce the maximum rate of tooth movement. The optimalorthodontic force can then be described as the force that, to the bestof scientific knowledge, is most effective in producing a desiredoutcome of a certain orthodontic treatment. This may be the force that,if applied to one or multiple teeth, would result in the maximum rate oftooth movement, while at the same time avoiding any adverse short orlong term tissue damage, minimizing patient discomfort or aiding inachieving any other desired outcomes. The force can be in any directionor around any axis in a three-dimensional space and can vary inmagnitude, direction, frequency, profile or point of application. Theoptimal orthodontic force can be patient specific, as well as age orhealth specific, and can further differ for each tooth, group of teeth,type of tooth movement or other type of treatment.

The use of new orthodontic appliances such as clear aligners,robotically bent arch wires or 3D printed orthodontic appliances andcomponents has provided new opportunities for controlling the amount oftooth movement and the forces applied to one or more teeth. The abilityof 3D printing orthodontic and dental appliances or parts thereof allowshighly accurate control of the shape and functionality of the appliance.For example, the exact forces of an orthodontic appliance could becontrolled by 3D printing by means of controlling the material type,stiffness, and geometry at a micron level. Similarly, the exact forcesapplied by an orthodontic arch wire can be controlled using roboticallybent and custom formed arch wires. While this technology provides asignificant improvement over conventional orthodontic systems, it canstill lead to orthodontic appliances that transmit forces to the teethwhich are higher than the optimal forces levels and thus can lead to thenegative effects including discomfort, tissue necrosis or rootresorption. The measurement data obtained from the measurement devicedescribed herein may be applied in the determination of a patient-,tooth-, health-, time- and/or treatment regimen-specific target forcefor use in the configuration and manufacture of such aligners.

Aspects of the present disclosure provide a device configured toquantify the biomechanical tissue response and behavior of the PDLin-vivo (e.g. by obtaining force and/or measurement data). Themeasurement device may for example include; an attachment mechanismallowing a user to apply and transfer a stimulus to one or more teeth; afeedback system including at least one sensor configured to measure thestimulus applied to one or more teeth; a feedback system including atleast one sensor configured to measure parameters relating to thebiomechanical tissue response (e.g. changes in position data) resultingfrom the applied stimulus; a power source configured to provide power tothe device; a processor in data communication with the sensors, saidprocessor being able to measure, process and analyze the signalsreceived from the sensors; and, a feedback mechanism in the form of ageneral user interface or indicator lights.

Aspects of the present disclosure provide a measurement device formeasuring and quantifying the biomechanical tissue response and relatingbehavior of the PDL and surrounding tissue in-vivo. The device includesa sensor system configured to measure the stimulus applied to the one ormore teeth using a six degrees of freedom force sensor system. In someembodiments, the sensor system is configured to measure thebiomechanical tissue response using magnetic hall sensors to measure theresulting biomechanical tissue response of one or more teeth. In otherembodiments, the sensor system is configured to measure thebiomechanical tissue response using a camera to measure the resultingbiomechanical tissue response of one or more teeth. In some embodimentsthe sensor system is configured to measure the biomechanical tissueresponse using computer vision to determine the position of certainpoints on one or more teeth and to measure the resulting biomechanicaltissue response. This may include using computer vision to calculate theposition of the tooth in 3D. The device may be configured to providefeedback to the user regarding the desired stimulus to be applied.

Aspects of the present disclosure provide a method for determining andquantifying the behavior of the periodontal ligament. The methodincludes using a measurement device to apply a stimulus that includescyclic forces, measuring the tissue response of one or more teeth foreach back and forth cycle, and quantifying and determining points on thecurve describing the PDL behavior

Aspects of the present disclosure provide a device for quantifying thebehavior of the PDL and measuring the biomechanical tissue responseresulting from an applied stimulus. The device includes a means forcreating an attachment to one or more teeth and for transmitting astimulus such as a force to the tooth. The device further includes a rodallowing a stimulus to be transferred from the handle of the device tothe tooth. The device includes an ergonomically shaped handle for theuser to hold the device and can include indicator lights or LED's, anLED or LCD screen to provide feedback to the user. The device can alsoinclude a sensor system to measure the biomechanical tissue response.

Optimal Force Engine

Above, reference is made to a target orthodontic force which is said tobe an optimal force or optimal orthodontic force.

An Evidence-Based Approach

Previous research on an optimal force has focused primarily on therelationship between the force magnitude and the resulting toothmovement. This relationship was expressed mathematically as

x=ƒ(F)  (Equation 1)

and is based on the general assumption that a quantitative relationshipcan indeed be determined. Equation 1 describes the tooth movement as afunction of a single input parameter, namely force F. This relationshipdisregards several of the relevant factors relating to orthodontic toothmovement, however, and therefore is unlikely to be able to fully andaccurately describe such a complex relationship. In contrast, there aremany different factors that have been shown to affect the resultingtooth movement. If these additional parameters are taken into accountthis would be expressed mathematically in the form:

x=ƒ(p ₀ ,p ₁ ,p ₂ , . . . ,p _(n))  (Equation 2)

where x is the resulting orthodontic tooth movement and p₀, p₁, p₂ . . .p_(n) are all parameters that to some extent affect said tooth movement.

Equation 2 thus takes into account all relevant parameters, but isdifficult to solve because too many variables remain unknown. The choiceof suitable variables is critical and can be narrowed down byconsidering (a) the controllability of input parameters and (b) themeasurability and quality of output parameters.

Only a number of input variables are directly controllable and can bealtered by the researcher or practitioner (i.e. “Controllable Factors”e.g. including static force magnitude, direction, duration, point ofapplication and frequency of application as well as the amplitude,frequency and profile of a dynamic force). Earlier factors (e.g. time<0factors) are part of the biological system and cannot be controlled,while later factors (e.g. time>0 factors) are in some way a function ofthe applied force F, and therefore can, to some extent, be controlled.

Various output variables can be used to measure tooth movement, but theattainable quality thereof often seems to be a decisive factor. Dataquality can be gauged in terms of:

-   -   Accuracy    -   Frequency (how often is data obtained)    -   Timing (when data is first obtained e.g. after t=1 sec, 1 day or        1 week)    -   Relationship to input parameter (data are directly or indirectly        related to F)    -   Robustness (data are susceptible to variations of measuring        method, type of tooth movement, individual differences, time of        sampling).

When reviewing the available literature, the above factors seem to havegoverned the success or failure of many a study. The availability andaccuracy of data were identified as key concerns, as well as largeindividual variations, the timing of measurements with regard to thedifferent stages of tooth movement, variations in species andcontrolling the type of tooth movement^(2,7,8).

Past Study Methods and their Limitations

Studies have frequently not been able to reach conclusive results andhave listed a lack of statistically significant data, insufficientdevice capabilities and study methods as reasons. The following sectionwill discuss some of the available research methods and the relatingchallenges faced by researchers.

In-vivo studies are attractive in that no compromise is made in terms ofthe accuracy of the system being examined. The affected tissues are intheir true state and the biological system retains its all-importantproperties that might affect orthodontic tooth movement. Yet, theaccuracy to which forces can be applied to individual teeth in-vivo isoften limited by appliance capabilities. The mechanical nature ofconventional appliances often does not provide the desired feedback andcontrol over the applied stimulus, which in turn affects the type oftooth movement.

The use of electronic test set-ups in-vivo has also been previouslydescribed⁹⁻¹¹. These allow for improved data collection, but are oftenlimited by practical applications. Electronic appliances, measuringdevices or experimental set-ups are often only suitable for laboratoryenvironments and require physical connections such as cables to acomputer for data collection. This makes repeated measurements harderand limits the attainable sampling frequency during treatment.

In-vitro studies on the other hand allow for sophisticated test set-upsand therefore improved means for data collection. The use of accuratemeasuring equipment enables characterizing the response of the PDL whensubjected to various force stimuli^(12,13). In-vitro experiments areunfortunately limited by the extent to which the tissue response ofspecimens resembles the true biological system response.

Developing mathematical models offers interesting insights and presentsitself as an attractive solution to define an optimal force. Aspreviously discussed, however, these rely heavily on the availability ofdata and its accuracy. Further, accounting for multiple variablesrequires increasingly complex models, while simplifying modelscompromises in accuracy by assuming the effect of certain parameters tobe negligible. Ren et al.¹ noted for example that when co-variables suchas reactivation or type of appliance were taken into account, thevariance of their model increased significantly. Such a model is thussensitive to uncertainties or unknown parameters and does not offerrobustness.

The use of finite element methods (FEM) for orthodontic studies hasprovided great insight to patterns of stress distribution in the PDL,tooth mobility, and also the time dependent changes incurred after theapplication of a force^(14,15). It can further account for differenttooth and root geometries and different material properties. Preparationof FEM models often requires CT scans¹⁴ as well as defining numerousmaterial properties, which play a critical role in the analysis.Assumptions need to be made regarding said material properties andaccurately defining these remains a challenge¹⁶.

The material properties of the PDL have for example frequently beenassumed to be linear elastic, homogeneous, and isotropic¹⁴ even thoughdata indicates that the morphology of the PDL is highly inhomogeneous³.Such simplifications cause the validity and reliability of FEM to bequestioned¹⁷. Current FEM models are also not well suited to accuratelyaccount for the time-dependent short and long term changes seen in thePDL.

Generally, all of the prior methods for investigating the concept of anorthodontic force have limitations and have led several researchers toconclude that improved technologies and methods are required. Themultitude of factors relating to orthodontic tooth movement and theirinterdependent nature make it impossible to isolate single variables.Without further discussion hereof, it is unlikely that all suchvariables and unknown parameters can be accounted for in an effort todetermine a quantitative relationship.

Observations

When collectively reviewing the available literature concerned with anoptimal orthodontic force, several factors which play a fundamental rolein conducting orthodontic research can be observed.

Observation No. 1—Risks of Quantitative Solutions

The general prior art approach to defining an optimal orthodontic forcehas been of a purely quantitative nature. Exact numerical values ofvariables have been used with the aim of determining statisticallysignificant solutions and mathematical models. As discussed, these donot offer robustness to variations and do not account for the manyunknowns of the biological system under investigation that researchersseem to acknowledge exist.

Observation No. 2—Non-Pathologic Tooth Mobility

The intra-alveolar displacement of a tooth is usually referred to astooth mobility and is more frequently related to periodontics. Toothmobility is also observable for a healthy tooth and is a function of thebio-physical parameters of the healthy periodontal environment. Itrepresents the intra-alveolar displacement of a single tooth due to anapplied force. Notably, non-pathologic tooth mobility is the only factorwhich is:

-   -   measurable in-vivo    -   measurable within seconds    -   measurable without causing any damage to the affected tissues    -   measurable by external means and without sacrificing the subject    -   measurable repeatedly and dynamically.

There are also two clearly distinguishable stages of tooth mobility,namely initial tooth mobility (ITM) and secondary tooth mobility (STM)as first described by Mühlemann¹⁸. ITM is generally perceived as beingfacilitated by the visco-elastic properties of the PDL, while STM isconsidered the result of an elastic deformation of the alveolar socketwalls^(12,18).

Observation No. 3—Force Magnitude

When considering previous studies on humans very few have accuratelyinvestigated the effect of low forces of 30 cN or less. The majority ofstudies listed in previous reviews by Owmann-Moll et al.⁶, Ren et al.⁷or Von Böhl et al.¹⁹ have examined forces that lie in the range of 50 cNto 200 cN, with some as high as 1500 cN.

Observation No. 4—Light Orthodontic Forces

Even though references to light orthodontic forces or comparisonsbetween light and heavy forces can repeatedly be found, there exists noclear definition as to what constitutes a light force.

The term can refer to forces ranging from 1 cN to 60 cN, while if twoforces are compared, even 300 cN can be referred to as light incomparison to a second larger force⁵. Despite its frequent use, there isno universal consensus nor sound scientific evidence regarding the forcemagnitude⁷. Any reference to a light force requires a threshold, amaximum value or reference point by which it can be gauged.

A New Paradigm

Non-pathologic tooth mobility can be measured as a function of anapplied force and represented as a curve which has been measured sincethe 1950s. The validity of this curve has been confirmed by severalstudies and simulations^(18,20-23). Tooth mobility further representsthe only externally measurable variable immediately after theapplication of a force and before any significant tissue remodeling hastaken place. It is thus one of the most important and yet disregardedfactors relating the applied force to orthodontic tooth movement.

Tooth mobility can be regarded as an intermediate variable when relatingan applied force to orthodontic tooth movement. The shape of the curveis directly dependent on the majority of factors relating to thebiological system as well as those of the applied force. It is afunction of the root geometry and morphology of the PDL and surroundingalveolar structures. It is also a function of the force magnitude, theforce direction and the rate of force application.

In contrast to the previously discussed models, characterizing the toothmobility curve does not require any knowledge of the variables by whichit is affected. Instead, it can be measured as was already done over 50years ago by Mülemann¹⁸. The resulting numerical value of the maximumdisplacement in μm is also not critical. Instead, the ability todetermine the shape of the tooth mobility curve is critical. The shapeallows the ITM and STM force ranges to be identified, thereby providingvaluable insight into the extent of PDL compression.

This argument is based on the typical curve that is observed when thesoft PDL is deformed either by compression or tension as described indetail by Sanctuary et al.¹². In both cases the change in displacement(often expressed as strain) per change in force is highest close to aforce of zero magnitude and decreases at higher forces⁴. The steeper ITMgradient for lower forces and a decreased gradient for STM at higherforces is also in agreement with the argument that low forces willinduce an intra-alveolar displacement of the root, while forcesexceeding the ITM range will displace both the PDL and the surroundingalveolar bone^(20,14).

Based on the above, the hypothesis is put forth that the force magnitudeat which the transition from ITM to STM occurs corresponds to a criticalforce F_(C) at which the PDL has, at least at one location, been fullycompressed.

Because the shape of the tooth mobility curve depends on all relevantfactors affecting tooth movement at the time of force application(time=0), the value of F_(C) is automatically also a function thereof.This is expressed mathematically in the form:

F _(C)=ƒ(force magnitude,type of movement,point of application,rootgeometry,PDL morphology,blood pressure,species,patientage,etc.)  (Equation 3)

where F_(C) is the critical force value in grams and all variables offunction ƒ in some way affect the shape of the measured tooth mobilitycurve. It is thus suggested that orthodontic tooth movement should beconsidered a function of F_(C) rather than a function of the initialvariables affecting F_(C). Equation 1 then becomes:

x=ƒ(F _(c))  (Equation 4)

where the value of F_(C) is completely case specific. For example Fc foreach of a Minipig, rat and human may be as follows:

F _(C)=ƒ(Minipig,Molar,Translation,Rotation)≈25 cN

F _(C)=ƒ(Rat,Molar,Translation,Rotation)≈0.5 cN

F _(C)=ƒ(Human,Incisor,Intrusion)≈50 cN

F _(C)=ƒ(Human,Molar,Translation,Tipping)≈13 cN

Even though these values are approximate and for illustration purposesonly, the above examples show that the value of F_(C) is case and toothspecific. The value for the human cases for example is significantlyhigher than that of the rat, while the value of F_(C) within the samespecies human is higher for intrusion than it is for thetranslation/tipping movement. It should be noted that the value of F_(C)could in itself be dynamic as it is influenced by blood pressure,remodeling of the PDL and its surrounding structures and othertime-dependent biological processes. The critical force F_(C) is not anabstract mathematical parameter but is related directly to physicalphenomenon. Any force with a magnitude exceeding F_(C) is likely to havea significantly different effect on the tissue remodeling than a forceof a magnitude below F_(C). The measurement and definition of such casespecific values of F_(C) could provide a key reference for the futureinvestigation of an optimal orthodontic force and orthodontic treatmentin general. In this paradigm, force magnitude alone is no longer theonly critical component but rather the force magnitude expressed interms of F_(C). Further, the definition of F_(C) would provide areference by which to define light orthodontic forces. A lightorthodontic force could be defined as a force of which the magnitude issmaller than F_(C). Once the value of F_(C) has been determined it, or aforce with reference to F_(C), can be related to the orthodontic toothmovement as described by Equation 4.

With this evidence-based approach in mind, the optimal force engine (25)described above with reference to FIG. 1A may be configured to performor conduct a methods for determining a target orthodontic force that canbe said to be the optimal orthodontic force. The optimal force engine(25) may be configured to define a target or optimal force based on oras a function of one or more determinate points of PDL behavior data(9). One example embodiment of a method for determining a targetorthodontic force is described below with reference to FIG. 29. Themethod may be conducted by a suitable computing device which in someembodiments may form a part of or may provide an optimal force engine.

The method includes receiving (502) patient data associated with apatient. The patient data may include patient characteristic data andpatient treatment data. The patient characteristic data may for exampleinclude data points relating to one or more of physiological,biological, genetic and situational characteristics of the patient. Thepatient treatment data may include data points relating to patientcondition and treatment requirements, including for example one or bothof a tooth type indicator and a tooth position indicator associated witha tooth of the patient to be corrected. The patient treatment data mayalso include treatment movement data (e.g. including one or moretreatment movement values) which describes the required tooth movementto effect treatment (or correction) of the tooth.

The method includes retrieving (504) PDL behavior data associated withat least a subset of the patient data. The PDL behavior data includes oris based on measurement data including force measurement data points andcorresponding displacement measurement data points having been measuredin-vivo while applying a force to a tooth of a human or animal subject(e.g. using a measuring system as described in the foregoing or othersuitable means).

The PDL behavior data may include one or both of measured PDL behaviordata and modelled PDL behavior data which is based on force measurementdata points and corresponding displacement measurement data pointshaving been measured in-vivo while applying a force to a tooth of ahuman or animal subject. In some cases, for example, retrieving the PDLdata may include querying a datastore (7) for data (including e.g.modelled and/or measured PDL behavior data) that is associated with thepatient data or a subset thereof. In other cases, retrieving the PDLdata may include using a PDL behavior model (33) to model or generatedata that is associated with the patient data or a subset thereof. Inother cases, the data may be retrieved from an interface module (23) orotherwise made available to the optimal force engine. The PDL behaviordata may therefore include or be based on historical PDL behavior curvesdetermined based on measurements of other humans. The PDL behavior datacan therefore include one or both of data points measured from anotherhuman and data points determined by a model that is trained based ondata points measured from another human. The PDL behavior data includesor is based on externally measurable data points, which may facilitateor simplify the collection of large-scale datasets by virtue of the databeing more easily obtainable (e.g. using a measurement system asdescribed in the foregoing). In some implementations, retrieving the PDLbehavior data includes retrieving raw data describing the PDL behaviorcurve consisting of the measurement of the applied force and themeasured resulting tooth displacement.

Referring to FIG. 19A, an example visualization of data describing thePDL behavior which can be received in real time or retrieved from memory(such as a datastore) is shown for one of six possible degrees offreedom. The data describes the behavior of the PDL when it is subjectedto an applied force, in this case to a back and forth motion, andincludes both the magnitude of the force and the displacement as well asthe direction of the force and the displacement (e.g. forward orbackward or clockwise or anticlockwise) for the relevant degrees offreedom. The data could also vary depending on any other variables inthe stimulus that have been applied to the one or more teeth. Forexample, the measurement data can include time dependent factors such asfrequency, or duration of application, and the data can typicallyinclude time data to describe the moment in time when each of the datapoints was measured, recorded or received by a processor. The data shownin the Figure can describe the behavior of the PDL when subject to astimulus and all measurable variables such as also the visco-elasticeffects, relating hysteresis, and any time dependent compression ortension or any other movement of the PDL.

Referring to FIG. 19B, another example of the raw measured datadescribing the behavior of the PDL is shown. The data shown is for aspecific patient and a specific tooth and in this example is measuredfor repeated cyclic back and forth motion of the tooth. The data shows16 consecutive back and forth movements denoted by C1, C2, . . . , C16,and the same can be measured for any number of cycles of movements. Thedata in the Figure also shows the change in the shape of the curve andmore specifically the increased displacement at the same force applied.Aspects of the present disclosure can use such raw measured data of thePDL to determine a critical force level and to determine the relatingoptimal orthodontic force.

Referring to FIG. 19C, another example of the raw measured data isshown. In this case the data includes more noise, but allows for thesame information describing the PDL behavior to be extracted. Variousmeans for data filtering, normalizing, averaging or any other dataprocessing can be applied to the raw measured data.

Returning to FIG. 29, retrieving (504) PDL behavior data may includeretrieving or determining (505) one or more determinate points or rangesfrom the PDL behavior data associated with the patient data or a subsetthereof. In some cases, the PDL behavior data may be in the form of aPDL behavior curve and the determinate points or ranges may be obtainedfrom the PDL behavior curve. In other cases, the PDL behavior data maybe obtained from a PDL behavior model (33) and determining thedeterminate points may include using the model to determine thesepoints. Each of the one or more determinate points includes a componentfor each of six degrees of freedom of movement (e.g. translation androtation about each of x-, y- and z-axis). Different mathematical orstatistical approaches can be used to determine a determinate range orpoint on the PDL behavior curve. In one example embodiment, the criticalforce level Fc is calculated. As described in the foregoing, this can bedone by using segmented linear regression for example and identifyingthe force at which the ITM (initial tooth movement) changes to STM(secondary tooth movement) or in other words, the force at which the PDLis compressed to a certain extent. Thus, in some implementations, theone or more determinate points include a critical force value. In otherimplementations, other determinate points may be used. The PDL behaviordata may thus include a reference point that reflects the physiological,biological, genetic and/or situational characteristics of the patient(by virtue of the association with patient data), in particular of thepatient's PDL. In other words, the reference point is one that describesa force that will produce a comparable effect on the alveolar structuresfor any patient (but which force will be different for differentpatients with different physiological, biological, genetic andsituational characteristics or properties). This allows for comparisonof treatment plans and results across different patients in a mannerthat acknowledges or accommodates the fact that different patients anddifferent teeth have different properties that result in different PDLbehaviors. For example, referring to the table below in which thecritical force is used as the determinate point, it can be seen that byusing a determinate point such as the critical force as a referencepoint allows for a comparison across teeth and/or species (or patients)in terms of which the effect of the force on the alveolar structures iscompared. This is in contrast to comparing the absolute value of theapplied force as has hitherto been the conventional approach and whichis meaningless given the vastly different effect that will result on thealveolar structures if the same force is applied to different teeth,different species or different patients.

TABLE I Applied force Applied force Case-specific study detailsConventional Evidence-based Species Tooth Direction Fc(g) approachapproach Rat Molar Translation 0.50 25 g = 50 × Fc 0.5 g = 1 × Fc HumanIncisor Intrusion 50 25 g = 0.5 × Fc 50 g = 1 × Fc Minipig MolarTranslation 25 25 g = 1 × Fc 25 g = 1 × Fc Human Incisor Tipping 12.5 25g = 2 × Fc 12.5 g = 1 × Fc

Referring to FIG. 24, and as described in the foregoing, an exampledetermination of determinate points according to aspects of the presentdisclosure is illustrated. A biomechanical tissue response plot measuredin accordance with aspects of the present disclosure can in general bedivided into different stages of non-pathological tooth movement. Thesetwo stages can be described as the initial tooth movement (ITM) stageand the secondary tooth movement (STM) stage. The ITM occurs at lowerforces while the STM stage occurs at greater force levels. One possibleway of repeatedly determining these stages of tooth movement is usingsegmented linear regression, for which a straight line is fitted to themeasured force-displacement data describing the biomechanical tissueresponse the result of which is shown in FIG. 24. In another exampleshown in FIG. 25, the measured biomechanical tissue response can beanalyzed to determine the direction of the tissue response as well asthe magnitude of the tissue response as a function of the appliedstimulus. By taking the first and second derivatives of the toothdisplacement with respect to the applied force, it is possible todetermine for example the ease of movement. This parameter which isobtained by taking the first order derivative could be indicative of theforce range which would be safe to apply to a tooth or within which nolong-lasting negative effects could be expected.

Returning to FIG. 29, the method includes determining (506) a targetorthodontic force value that is specific to the patient data byinputting the PDL behavior data (which may be the determinate point(s)obtained from the PDL behavior data) into an algorithm which determinesthe target orthodontic force based on the PDL behavior data. Thealgorithm includes one or more of time, magnitude, case, patient (e.g.including patient characteristic and patient treatment) components andthe like. The algorithm may for example be expressed as an equation witha variable for each of the one or more time, magnitude, patientcharacteristic and patient treatment components as well as for therelevant determinate point(s). The patient treatment component may forexample include variables for one or more values representing one orboth of desired patient treatment outcome and patient treatmentobjective (e.g., as defined by treatment movement data). Determining thetarget orthodontic force value may therefore include defining the targetorthodontic force value as being a function of the one or moredeterminate points or ranges of the PDL behavior data (such as acritical force Fc value). The algorithm, or function, may be anysuitable binary relation over a first (target orthodontic force) set anda second (determinate point) set that associates to every element of thefirst set exactly one element of the second set. In other words, thealgorithm sets how the target orthodontic force value depends on thedeterminate point. The algorithm can be a linear or polynomial function,or any other relationship between the target orthodontic force value andthe determinate point(s), so that the target orthodontic force isdefined as, in the case of the critical force value being thedeterminate point:

Ftarget=ƒ(Fc)

It should however be noted that Fc can be replaced by any otherrepeatedly identifiable point, or range on the PDL behavior curve. Forexample a determinate point could be defined at the neutral position ofthe curve, at a point at which the curve reaches a certain gradient, orchange of position as a function of the applied force, the point atwhich a certain percentage of the maximum measured force or displacementis reached, an area, range or point at which the area under the curvereaches a certain value or any other definition with relation to themeasured PDL behavior curve.

The method includes outputting (508) the target orthodontic force value.The target orthodontic force value may be output for retrieval by one ormore of: a point of care system (5); a treatment plan engine (41); anappliance configuration engine (43) and an interface module (23) (e.g.for retrieval by any other system that might need the target orthodonticforce). The target orthodontic force value may be output for use ineffecting correction of the tooth of the patient (e.g. by formulating atreatment plan and/or configuring an orthodontic appliance to apply orexert the target orthodontic force). The target orthodontic force valuemay for example be output to an appliance configuration engine (43)configured to use the target orthodontic force value to configure one ormore parameters of an orthodontic appliance such that an orthodonticappliance manufactured or otherwise produced in accordance with theconfiguration applies the target orthodontic force to the tooth of theparticular patient. The target orthodontic force is a measurable valuethat can be compared to other forces for evidence-based treatment oforthodontic conditions. The target orthodontic force is based onexternally measurable parameters (i.e., the determinate points of thePDL behavior data). Specification of treatment plans and/or orthodonticappliances based on a target orthodontic force allows for a force-basedapproach to orthodontic treatment as opposed to purely spatial-basedapproach.

Referring to FIG. 30, an example target orthodontic force, also referredto as an optimal orthodontic force, as a function of Fc that varies withtime is illustrated. The optimal orthodontic force can take into accountthe time-dependent changes in the critical force level Fc, which changesover time and specifically throughout the duration of orthodontic toothmovement, and can be a function of time so that

Fopt=ƒ(Fc,t) and Fc=ƒ(case,t)

Fopt=ƒ(F(case,t),t)

In FIG. 30, an optimal force Fopt is shown for a series of differentorthodontic appliances worn during treatment. This can be a series ofarch wires, elastic or orthodontic aligners, for each of which anoptimal orthodontic force is determined. The first appliance has ahigher Fopt for example to initiate tooth movement, while the second andthird appliances have a lower Fopt so as to reduce the pressure on thePDL and alveolar tissue which is undergoing remodeling to facilitatetooth movement. Towards the end of treatment Fopt for each of a seriesof appliances is increased again as a patient becomes more accustomed tothe forces for example. The time dependent profile and optimalorthodontic force for each appliance can take on any profile that variesover time. In addition, FIG. 30 shows a time-dependent optimalorthodontic force for each of a series of appliances for two differentteeth, Tooth A and Tooth B. Similarly, Fopt can vary for any casespecific factors including one or more of patient age, gender, tooth, orgroup of teeth, treatment movement data, force magnitude and directionof force and the like.

FIG. 31 shows another example optimal orthodontic force Fopt as afunction of time. While FIG. 30 showed an optimal orthodontic force foreach of a series of orthodontic appliances, FIG. 31 shows a continuousdefinition of Fopt. As described above, Fopt can be defined using analgorithm (which may e.g. be a mathematical model, artificialintelligence, or any computer based model) to define such a time andcase specific function for Fopt. This graph can be determined for aspecific individual or group of individuals and can be based on, but notlimited to, measurements of the PDL behavior curve, patient feedback, ortreatment outcomes.

The method may include updating the algorithm using feedback data (15)including one or more of: test rig feedback data points, patientfeedback data points and treatment feedback data points. The feedbackdata is obtained during use or testing of an orthodontic applianceconfigured to apply the target orthodontic force. The feedback data mayfor example be based on or include rate of change data points measuredwhile using the orthodontic appliance. The algorithm may be updated ifit is not stored or otherwise identifiable as an optimal algorithm.Updating the algorithm may include iteratively updating the algorithm tooptimize the feedback data to determine an optimized algorithm fordetermining the target orthodontic force (which would then be an“optimal orthodontic force”). Iteratively updating the algorithm untilit is optimized means that it can be said that the target orthodonticforce determined using the optimized algorithm becomes the optimalorthodontic force as it is one that when applied by an orthodonticappliance optimizes a balance of patient comfort and treatment outcome,for example.

For example, referring now to FIG. 32, updating the algorithm usingfeedback data may include configuring (520) an orthodontic appliance toapply the target orthodontic force (this may include using an applianceconfiguration engine and inputting into the appliance configurationengine the target orthodontic force). This may be preceded bydetermining or creating a treatment plan based on the target orthodonticforce (e.g. using a treatment plan engine). The orthodontic appliancemay then be tested (522) on a test rig to compare the target orthodonticforce against the actual force applied by the appliance. Feedback datapoints from the test rig, including for example the extent to which theactual force matches the target orthodontic force, are collected intofeedback data (15). Feedback data for testing an orthodontic appliancecan include force data including six degrees of freedom and can also betime dependent. Further feedback data can include displacement data fortesting the orthodontic appliance and the displacement data can also betime dependent. Feedback data can further include both force anddisplacement data at one or more points in time and can be for a singletooth or a group of teeth. The orthodontic appliance may then be tested(524) for patient feedback, for example by fitting the orthodonticappliance to the relevant tooth or teeth of the patient and collectingpatient feedback data points for inclusion in the feedback data (15).

FIG. 33, for example, shows an example embodiment for obtaining patientfeedback data points using a linear scale (532) on which the level ofdiscomfort is indicated with relation to an orthodontic force as afunction of Fc (in this example illustration). This method can be usedto determine an optimal orthodontic force that achieves the least levelof discomfort while still resulting in orthodontic tooth movement. Inthis method, a force is applied to a tooth of a patient and the level ofdiscomfort experienced by the patient is indicated on the scale. Theforce as a function of the critical force Fc is then indicated andcorrelated to the level of discomfort experienced. A similar method canbe used to plot the force as a function of case specific Fc against thelevel of discomfort on a vertical axis. Other techniques or methods forcollecting patient feedback data points may also be used.

In an embodiment incorporating patient feedback data points obtainedusing the linear scale (532) of FIG. 33, and for example scoring rate oftreatment on a scale of 1 to 10, iteratively updating the algorithmbased on the feedback data points may for example include:

Patient discomfort Treatment feedback Sample Algorithm score score 15*F_(c) 19 (Very high) 4 (slow) 2 2*F_(c) 16 10 (rapid) 3 1*F_(c) 12 7 40.5*F_(c )  7 5 5 0.1*F_(c ) 3 (very low) 5 (slow)

The second sample of the algorithm may be determined as being optimizedin that it balances patient feedback data points against treatmentfeedback data points to result in the most rapid treatment that issufficiently comfortable from the patient's perspective. The examplealgorithm includes only a magnitude component (i.e. a multiplication ofthe target force by a fixed value). It should however be appreciatedthat in other embodiments, the algorithm may include other components soas to be time, stage and/or patient data dependent or the like. Theprocess of optimizing the algorithm may be more sophisticated and mayinclude for example time dependent functions such that the target forcechanges over time, may include exponential or logarithmic functions, orany other type of mathematical function that may define the target andoptimal force. Of course other forms of patient feedback may beobtained, including for example feedback on questions such as “How doesit feel while you chew?”, “Would you recommend this appliance to afriend?”, “Have you noticed any adverse effects, such as reddening ofthe gums, etc.?” and the like).

Returning to FIG. 32, the method includes testing (526) the appliancefor treatment feedback and collecting data points for inclusion in thefeedback data (15). Testing the appliance for treatment feedback mayinclude monitoring treatment progress (e.g. rate of movement), recordingtreatment milestones and the like. These data points may be collectedover a period of time, for example over hours, days, weeks or months.The feedback data can then be used to update the algorithm for thepurpose of optimizing one or more data points of the feedback data. Inother words, the algorithm may be updated to improve feedback datapoints against metrics such as patient comfort, rate of treatment andtreatment outcome.

Collecting feedback data (15) (particularly test rig and patientfeedback data points) may include collecting feedback data in real-timeor near-real-time for rapid iteration and updating of the algorithm. Inother words, utilizing additive manufacturing techniques to manufactureorthodontic appliances configured to apply the relevant targetorthodontic force allows for test rig feedback and patient feedback tobe obtained for a number of successive iterations of the algorithmwithin a relatively short space of time. There could for example bemultiple iterations analyzed for this type of feedback data within asingle day. Collection of treatment feedback data points may of courseneed more time as treatment has to be allowed to run its normal course.

The method may include determining (527) whether or not the feedbackdata is optimized. In one example the method may include collectingpatient feedback data and treatment feedback data where the patientfeedback data may include the level of discomfort of the appliance andthe treatment feedback data may include the rate of tooth movement ofone or more teeth. If the patient feedback data indicates that there wasvery low or no levels of discomfort experienced by the patient and thetreatment feedback data indicates a slow rate of tooth movement, thenthis could be considered not optimized and the algorithm may be updatedto increase the target force to be more optimal. In another example, ifthe patient feedback data indicates a high level of discomfort and thetreatment feedback data indicates a slow rate of tooth movement thenthis may be considered not optimized and the algorithm may be updated todecrease the target force level in order to achieve a more optimalpatient feedback and treatment feedback.

If (527) the feedback data is not optimized, the method includesupdating (510) the algorithm, for example by adjusting weights of theone or more components or variables of the algorithm. Similarly updatingthe algorithm, in cases where the algorithm includes a time component,may include updating the algorithm, which is a function of time, so thatthe target force is lower during a certain time during the treatment,for example when the tissue is undergoing remodeling, and is higher atanother time during treatment, for example at the end of treatment. Thealgorithm may be updated iteratively to maximize feedback metrics forimproved comfort, rate of treatment and/or treatment outcome. With thealgorithm updated, an updated target orthodontic force may be determined(528) using the updated algorithm and then used to reconfigure (530) theappliance (or create a new appliance) to apply the updated targetorthodontic force. The method may repeat (531) to obtain updatedfeedback data (15) until the one or more data points of the feedbackdata are optimized (527).

If or once (527) the feedback data have been optimized, the algorithmmay be stored (536) or otherwise labelled or identified as the optimalalgorithm for use in determining a target orthodontic force that is anoptimal orthodontic force based on one or more determinate points of PDLcurve associated with patient data or a subset thereof.

Aspects of the present disclosure therefore provide for thedetermination of a target orthodontic force value that is patient, caseand tooth specific and is optimized for patient comfort and/or treatmentoutcome. The tooth/patient/case specificity is by virtue of the targetorthodontic force being a function of PDL behavior data that isassociated with patient data. The optimization is by virtue of thealgorithm that has been iteratively optimized based on feedback data andthat operates on the PDL behavior data to output the target orthodonticforce. Aspects of the present disclosure enable a shift from a spatialparadigm of the prior art systems where treatment is determined based onrequired movement to a force-based paradigm for orthodontic treatmentwhere treatment is based on a relatable force applied on a determinatepoint of patient, case and tooth specific PDL behavior data.

Conventionally, the absolute value of orthodontic forces are compared toone-another and a single method has been used in numerous scientificstudies and publications looking to establish a relationship between theapplied force and, e.g., the rate of tooth movement. This approach ofcomparing the absolute value of the force is not able to account for anyvariation between the cases, the individuals, type of teeth or directionof movement. Aspects of the present disclosure, however, are able toaccount for such case specific parameters by defining the applied forceas a function of the case specific critical force Fc (or other suitabledeterminate point(s)). FIG. 34 illustrates one example embodiment ofcomparing forces according to aspects of the present disclosure. A firststep identifies (540) the absolute force magnitudes for two differentcases. For example the same absolute force magnitude of F=25 gm could beapplied to two different teeth (tooth A and tooth B), each having a casespecific critical force level (e.g. Fca=25 gm and Fcb=12.5 gm). The nextstep involves expressing (542) the absolute force magnitude as afunction of the critical force Fca and Fcb. If in the given example F=25gm which is equal to the critical force Fca and thus F=f(Fc)=1*Fca.However, it is twice the magnitude of the critical force Fcb and thuscan be expressed as F=f(Fc)=2*Fcb. In the third step, the force Fapplied to the two different cases A and B is compared (544) bycomparing the force F as a function of the critical force Fc:

F=Fa=ƒ(Fca)=1*Fc

F=Fb=ƒ(Fcb)=2*Fc

thus

Fa/Fb=(1*Fc)/(2*Fc)=½

2*Fa=Fb

Even though the absolute magnitude of the force F is the same in bothcases, the effective force for case B is twice as large as that in caseA. Practically, this process can be used to compare the same forceapplied to a molar (case A) vs for example an incisor (case B) for whichthe critical force level is lower. The force magnitude is the same butthe effect on the PDL and surrounding tissue might be much larger (twicethat) of the same force applied to the molar in case A. This example isdescribed with reference to an example scenario in which the relevantdeterminate point of the PDL behavior curve is the critical force. Itshould be appreciated that in other cases other points or ranges ofpoints may be used.

Model Training Engine

The model training engine (31) described above with reference to FIG. 1Amay be configured to perform or conduct a method for training a modelfor use in determining PDL behavior data points. This may includeassimilating patient- and case-specific values of a critical orthodonticforce for modelling periodontal ligament behavior. One exampleembodiment of a method for training a model for use in determining PDLbehavior data points is described below with reference to FIG. 35. Themethod may be conducted by a suitable computing device which in someembodiments may form a part of or may provide a model training engine.

The method includes obtaining (560) a dataset including or based on oneor both of measured PDL behavior data (9A) and associated metadata (11).This may include retrieving the dataset from a datastore (7) orcompiling the dataset from data retrieved from the datastore (7). Forexample, the dataset may include a subset or selection of measured PDLbehavior data (9A) and associated metadata. Alternatively, the datasetmay include all of the available measured PDL behavior data andassociated metadata. In some cases, the data set includes measured PDLbehavior data and modelled PDL behavior data (or subsets/selectionsthereof).

The measured PDL behavior data (9A) includes force measurement datapoints and corresponding displacement measurement data points havingbeen measured in-vivo while applying a force to a particular tooth of ahuman or animal subject. The metadata is associated with the human oranimal subject from whom the measurements were taken and includes datapoints relating to one or more of physiological, biological, genetic andsituational characteristics of the human or animal subject. The measuredPDL behavior data and associated metadata may be captured or obtained bya measurement system (10) or may be made available to the datastore viaanother suitable mechanism.

The data points of the measured PDL behavior data are associated withthe human or animal subject from whom they are obtained and with thetooth in respect of which they relate (i.e. in respect of which theforce is applied and of which the displacement is measured). In oneexample implementation, the measured PDL behavior data may include anarray for each subject and each tooth, the array including force anddisplacement measurement datapoints and associated timestampscorresponding to the time (real-clock or otherwise) and optionally dateat which the measurements were made. As mentioned in the foregoing, thedata points have components for each of six degrees of freedom. Forexample:

-   -   PDL_data(subject_ID, tooth_ID):    -   [yyyymmddhhmmssms, Ftx, Fty, Ftz, Dtx, Dty, Dtz, Fmx, Fmy, Fmz,        Dmx, Dmy, Dmz;    -   . . . ;    -   . . . ]

Each displacement measurement data point of the measured PDL behaviordata may therefore be associated with the particular tooth of aparticular subject to which a particular force is applied. In thecontext of PDL behavior, the displacement measurement data points may beconsidered dependent variables while the force measurement data pointsmay be considered independent variables. Although the term “displacementmeasurement data point” is used herein, it should be appreciated thatthis data point may in fact be a position measurement data point whichtogether with at least one other position measurement data point resultsin a displacement measurement data point. The term “displacementmeasurement data point” should therefore be interpreted to include oneor more position measurements. The dataset may therefore includemeasured PDL behavior data and associated metadata for each of a largenumber of human or animal subjects from whom or on whom the measurementsare obtained in-vivo. The dataset may be a large-scale data setincluding datapoints for hundreds of thousands of human and/or animalsubjects. PDL behavior is based on physiological attributes, someasuring data points relating to PDL behavior (such as theforce/displacement measurements) implicitly records physiologicalattributes without invasion or complexity that would otherwise berequired to understand PDL behavior.

The data points may be associated with timestamps relating to the timeat which the measurements were made. The data may for example includedata points measured for a longer period of time (e.g. 3 hours or even24 hours) with all data points being part of one continuous stream. Insome cases, the data points may for example be measured on a tooth onDay 0 for a duration of 3 minutes, then stop, then on Day 5 we can takethe same measurement for 3 minutes, then stop, then on Day 10 measurefor 3 minutes, then stop, etc. The data can be used as one time seriesdata set or each one of those 3 min data sets can be used individuallyfor training a model. The data points may therefore be a continuous datastream with intermittent instances of that data stream over a longperiod of time, or could be intermittent data. As the bone starts toremodel, the tissue may become inflamed and based on this the curve maychange to account for the changes of time.

The metadata points may include scores or other indicators ofphysiological, biological, genetic and situational characteristics ofthe human or animal subject. Selected physiological data points mayinclude any physiological parameters affecting the PDL behavior, such asdata points relating to one or more of: tooth and root morphology; PDLthickness and shape; blood pressure; health related data such as oraland tissue health, time in treatment journey (if applicable) and thelike. Selected situational data points may include one or more of:socioeconomic status; one or more living standards measure (LSM) inputs;an LSM output; country, state, city of residence; country, state, cityof birth; diet and the like. Selected biological data points may includeone or more of: age; gender; ethnicity; species and the like. Themetadata is associated with the human or animal subject from which themeasured PDL behavior data is obtained, for example:

-   -   Metadata(human_ID):    -   [gender, socio_economic, . . . , health, age, diet,        country_of_birth, country_of_residence . . . ]

The metadata may therefore include information about the human/animalsubject for categorization of and comparison between different types ofhuman/animal subjects. Characteristics or parameters such as these atleast to some extent affect the PDL behavior and are related toorthodontic tooth movement. By including these characteristics orparameters in the dataset, a learning model can learn the behavior ofthe PDL based on the included characteristics/parameters and associatedmeasurement data.

In some embodiments, the dataset therefore includes or is based on oneor both of: measured PDL behavior data including data points having beenmeasured in-vivo while applying a force to a particular tooth of each ofa plurality of human or animal subjects; and, associated metadata foreach of the plurality of human or animal subjects. Further, in someembodiments, the dataset includes or is based on one or both of:measured PDL behavior data including data points having been measuredin-vivo while applying a force to each of a plurality of teeth of ahuman or animal subject; and, the associated metadata for the human oranimal subject. In other embodiments, datasets may be retrieved on aper-tooth or per-subject basis.

The dataset may be a training dataset including one or more targetattributes (i.e. that which is to be predicted) which, depending onimplementation, may be either one or more force measurement data points,or one or more displacement measurement data points.

Obtaining the dataset may therefore include receiving the measurementdata of an applied force and the measurement data of the resulting toothdisplacement, such movement can be one or any combination of six degreesof freedom. The measurement data describing the PDL behavior can includethe magnitude, direction, timestamps, or other parameters describing theapplied stimulus and resulting tooth movement and can include data forone individual or a group of teeth.

The method includes training (562) a model using a learning algorithmwhich identifies patterns in or relationships between data points of thedataset. This may include identifying a relationship between forcemeasurement data points and displacement measurement data points for, insome implementations, different teeth and/or different characteristicsof the subject. Training the model may include using the learningalgorithm to identify patterns in or relationships between the measuredPDL behavior data points and/or metadata data points.

As mentioned above, the dataset may include one or more input attributesand one or more target attributes and the learning algorithm may beconfigured to find patterns in the dataset that map the one or moreinput attributes to the one or more target attributes. In one exampleimplementation, the input attributes include one or more metadata datapoints and the other of: one or more force measurement data points, orone or more displacement measurement data points.

The method may include performing (564) feature processing to transformone or more data points of the dataset into features and compiling (566)the features into one or more feature vectors. This may includeprocessing the measured PDL data and/or metadata to determine featuresfor inclusion in the one or more feature vectors. The one or morefeature vectors may include features relating to one or more of: forcemeasurement data points, displacement measurement data points andmetadata data points. Examples of features may include the forcedirection, force duration, rate of change of force, the gradient of theforce data in relation to the displacement data, hysteresis effects, orrecovery rates of displacement data amongst others. Training the modelusing the learning algorithm may then include using the learningalgorithm to identify patterns in or relationships between the featuresin the one or more feature vectors. Training the model may furtherinclude determining feature weights for features of a feature vector.The method may therefore include creating a feature vector of thephysiological parameters or characteristics and the tooth displacementof the one or more teeth for which the PDL behavior is described.

In some implementations, training the model using the learning algorithmincludes training a plurality of different models using differentlearning algorithms and generating different feature vectors. Atooth-to-tooth feature vector including measured PDL behavior data fordifferent teeth may for example be generated using a linear regression,segmented linear regression or similar algorithm for identifying arelationship in PDL behavior data between teeth. A rich feature vectorincluding metadata-based features and PDL behavior-based features may begenerated using a convolutional neural network (CNN) or similar modelfor identifying a relationship between these features. The model, whichmay be a machine learning model, is therefore trained to predict thetooth movement resulting from an applied force or stimulus to the one ormore teeth (or vice versa) and may include a number of feature weights.

The method includes outputting (568) the model (33) for use indetermining PDL behavior data points including required force datapoints for a desired displacement of a tooth of a patient or resultantdisplacement data points for an applied force to a tooth of a patient.Outputting the model may include outputting the feature weights forfeatures of the one or more feature vectors. The model that is output isa computer-implementable artifact that models PDL behavior for a patientbased on one or more of physiological, biological, genetic andsituational characteristics of the patient. Outputting the modelincludes saving the model and optionally feature weights for future use.The final model is suitable for outputting a prediction of the behaviorof the PDL when subject to a specific force or stimulus. The model maybe output for access and use by a treatment plan engine (41), anappliance configuration engine (43) and optionally a point of caresystem (5) or other remote computing system via the interface component(23).

Referring now to FIG. 36, an example method for using a PDL behaviormodel (33) is illustrated. The method may be conducted by a computingdevice. The method includes receiving (570) a force vector data for oneor more teeth, where such data can include the force magnitude,direction, point of application and can be in six degrees of freedom. Inaddition, such force data can describe the forces applied for a singleor a group of teeth. The force vector data is input into the PDLbehavior model (33) which uses the input to compute (572) and return anestimate for the behavior of the PDL and the related tooth displacement.Such resulting tooth displacement can be case, tooth and patientspecific and can be for one or more teeth. The method includes returning(574) the estimated PDL behavior data and related tooth displacement foruse in a software, which can be a treatment planning software, orappliance design software or other.

Aspects of the present disclosure therefore provide a process fordeveloping a machine learning model to describe the behavior of the PDLand a process of using a trained machine learning model to determine anestimate of the behavior of the PDL when subject to a given stimulus.Aspects of the present disclosure relate to the assimilation of patient-and case-specific values of a critical orthodontic force. Inputparameters may include metadata and patient information such as age,gender, ethnicity, and other factors. The corpus of critical orthodonticvalues used to produce output parameters relating to orthodontictreatment, including orthodontic treatment planning, staging of toothmovement, determining the maximum rate of tooth movement, duration oftreatment and a model for clear aligners or other orthodonticappliances. This may include the use of Machine Learning (ML) andArtificial Intelligence (AI) methods to determine additional casespecific values of critical orthodontic forces using the corpus ofcritical orthodontic values as input.

Treatment Plan Engine

Aspects of the present disclosure relate to methods and systems fororthodontic staging using critical orthodontic force parameters,including for example, automating and optimizing an orthodontictreatment plan staging based on critical orthodontic force data or otherPDL behavior-based determinate points. The system and method may useknown critical orthodontic force data or other PDL behavior-baseddeterminate points as input. Outputs may include maximum tooth movementrates, optimal force to be applied to one or more tooth/teeth, aduration of treatment, a number of iterations required for orthodonticaligner treatment and an optimal path of movement of one or moretooth/teeth.

The treatment plan engine (41) described above with reference to FIG. 1Amay for example be configured to perform or conduct a method fororthodontic treatment staging. This may for example include automatingand optimizing an orthodontic treatment plan staging based on criticalorthodontic force data or other PDL behavior-based determinate points.

FIG. 37A is a flow diagram which illustrates one example embodiment of amethod for orthodontic treatment staging according to aspects of thepresent disclosure. The method may be conducted by a suitable computingdevice which in some embodiments may form a part of or may provide atreatment plan engine.

The method includes receiving (702) patient data (13) including patienttreatment data and patient characteristic data. The patient data may bereceived from a point of care system (5) via a communication network(21) and/or interface component (23). The patient data may for examplebe input into a point of care system by a healthcare professionalresponsible for the treatment or correction of the patient's tooth orteeth. In other embodiments, the patient data (13) may be received fromanother source, such as a datastore (7) an interface component (23) orthe like.

The patient characteristic data may for example include data pointsrelating to one or more of physiological, biological, genetic andsituational characteristics of the patient. The patient treatment dataincludes one or both of a tooth type indicator and a tooth positionindicator associated with the tooth of the patient to be treated andtreatment movement data which describes the required tooth movement toeffect treatment or correction of the tooth. The treatment movement dataincludes one or more treatment movement values, each treatment movementvalue having components for each of six degrees of freedom.

In some embodiments, receiving patient data includes determining (704)treatment movement data for the patient. Determining treatment movementdata may include determining required movement of the tooth based on thetooth's current condition or situation and its desired (or treated orcorrected) condition or situation. Receiving patient treatment data mayfor example include receiving a tooth model with individual teeth andthe planned movement of each individual tooth and using the model todetermine the treatment movement value including direction and magnitudeof movement of each individual tooth.

Receiving patient data thus includes receiving information about theintended movement of a specific tooth. This information may include thetype of tooth and can include other patient data including the patientage, health, geographical location or ethnicity (as contained in thepatient characteristic data).

The method includes determining (706) one or more stage movement valuesand corresponding stage force values for each of one or more stages of atreatment plan. The sum of the one or more stage movement valuesapproximates the treatment movement value such that treatment of thetooth is effected through completion of each of the stages of treatment.Each stage movement value and its corresponding stage force value isdetermined based on a target orthodontic force value, which is in turnbased on one or more determinate points of PDL behavior data associatedwith at least a subset of the patient data. As mentioned in theforegoing, the PDL behavior data includes or is based on forcemeasurement data points and corresponding displacement measurement datapoints having been measured in-vivo while applying a force to aparticular tooth of a human or animal subject. FIGS. 37B and 37C areflow diagrams which illustrate two example embodiments for determiningone or more stage movement values and corresponding stage force valuesbased on a target orthodontic force value. As with the treatmentmovement value, each of the one or more stage movement values, stageforce values and target orthodontic force values include components foreach of six degrees of freedom. The treatment movement value, stagemovement value, stage force value and target orthodontic force value maybe time dependent.

The method includes outputting (708) the one or more stage movementvalues and corresponding stage force values as a treatment plan fortreatment of the tooth. The stage movement values and stage treatmentvalues may be output to one or more of: a point of care system (5), adatastore (7), an appliance configuration engine (43) and an interfacemodule (23).

Referring now to FIG. 37B, in which a first example embodiment of amethod for determining one or more stage movement values andcorresponding stage force values based on a target orthodontic forcevalue is illustrated.

In this example embodiment, the method includes determining (720) atarget orthodontic force value based on one or more determinate pointsof the PDL behavior data associated with at least a subset of thepatient data. In embodiments in which the PDL behavior data includes thePDL behavior model, determining the target orthodontic force value mayinclude using the PDL behavior model to determine the one or moredeterminate points of the PDL behavior data and inputting the one ormore determinate points into an algorithm that determines the targetorthodontic force as a function of the one or more determinate points.Further inputs to the algorithm may include data points relating to oneor more of time, magnitude, patient characteristic and patient treatmentcomponents of the algorithm. As mentioned in the foregoing, thealgorithm may be optimized by iteratively updating the algorithm tooptimize feedback data such that the target orthodontic force is anoptimal orthodontic force. The feedback data may include one or more of:test rig feedback data points, patient feedback data points andtreatment feedback data points. The feedback data is obtained during useor testing of an orthodontic appliance configured to apply a targetorthodontic force.

The method includes using (722) the target orthodontic force value todetermine a corresponding stage movement value. Determining thecorresponding stage movement value may include using the PDL behaviordata associated with the patient data. For example, in the case of thePDL behavior data being a PDL behavior model (33), this may includeinputting the target orthodontic force value into the PDL behavior model(33) which in turn outputs movement data which corresponds to the targetorthodontic force value. The movement data may be an approximation orestimation of the tooth movement that would result if the targetorthodontic force were applied to the particular tooth of the particularpatient.

The method includes summing (723) the stage movement values andcomparing (724) the sum of the stage movement values to the treatmentmovement value and, if (726) sum of the stage movement values is lessthan the treatment movement value, the method repeats includinginitializing (728) a next treatment stage and determining (720, 722),for the next treatment stage, a target orthodontic force value and acorresponding stage movement value until the sum of stage movementvalues approximates the treatment movement value. If (726) or once thesum of the stage movement values approximates or equals the treatmentmovement value, the stage movement values and stage movement forces arestored (730) for output as a treatment plan for the patient.

FIG. 37C illustrates a second example embodiment of a method fordetermining one or more stage movement values and corresponding stageforce values based on a target orthodontic force value.

The method includes initializing (740) a stage movement value beingequal to the treatment movement value.

The method includes using (742) the stage movement value to determine acorresponding stage force value based on PDL behavior data associatedwith at least a subset of the patient data. Determining thecorresponding stage force value may include using the PDL behavior dataassociated with the patient data. For example, in the case of the PDLbehavior data being a PDL behavior model (33), this may includeinputting the stage movement data into the PDL behavior model (33) whichin turn outputs the corresponding stage force value. The force value maybe an approximation or estimation of a force required to be applied tothe tooth in order to result in or achieve movement of the particulartooth as described by the stage movement value.

The method includes determining (744) a target orthodontic force valuethat is based on one or more determinate points of the PDL behaviordata. In embodiments in which the PDL behavior data includes the PDLbehavior model, determining the target orthodontic force value mayinclude using the PDL behavior model to determine the one or moredeterminate points of the PDL behavior data and inputting the one ormore determinate points into an algorithm that determines the targetorthodontic force as a function of the one or more determinate points.Further inputs to the algorithm may include data points relating to oneor more of time, magnitude, patient characteristic and patient treatmentcomponents of the algorithm.

As mentioned in the foregoing, the algorithm may be optimized byiteratively updating the algorithm to optimize feedback data such thatthe target orthodontic force is an optimal orthodontic force. Thefeedback data may include one or more of: test rig feedback data points,patient feedback data points and treatment feedback data points. Thefeedback data is obtained during use or testing of an orthodonticappliance configured to apply a target orthodontic force.

The method includes comparing (746) the corresponding stage force valueto the target orthodontic force value.

If (748) the corresponding stage force value exceeds the targetorthodontic force value, the method includes defining (750) a new stagemovement value which is less than the previous stage movement value anddetermining (742) a new corresponding stage force value based on the PDLbehavior data for comparison (746) against the target orthodontic forcevalue. The method will therefore repeat with successive new stagemovement values until the corresponding stage force value approximatesthe target orthodontic force value. This may include re-determining thetarget orthodontic force value each time a new stage movement value isdefined.

If or once (748) the stage force value approximates the targetorthodontic force value, the method includes storing (752) the stagemovement value and corresponding stage force value that approximates thetarget orthodontic force value for use in a treatment plan. The methodmay further include determining a number of required treatment stages bydividing the treatment movement value by the stage movement value so asto determine the number of stages required to effect treatment of thetooth by applying the target orthodontic force value for each stage.

The methods described above with reference to FIGS. 37A and 37B or 37Cmay be conducted for each tooth to be treated such that a case-, tooth-and patient-specific stage movement value and corresponding stage forcevalue are determined for each tooth to be treated.

The methods described above provide for orthodontic staging using atarget orthodontic force that is determined based on PDL behavior datathat is case-, tooth- and patient-specific. FIG. 38 is a chart whichillustrates example plots of a stage force value and associated targetorthodontic force value over time for a plurality of stages oforthodontic treatment according to aspects of the present disclosure.

Appliance Configuration Engine

Aspects of the present disclosure relate to methods and systems fordetermining orthodontic appliance models using critical orthodonticforce data as input. Outputs of the method include a materialspecification, size, shape, thickness, direction and geometry offeatures (e.g. ridges, cuts, ribs, bumps, springs, hatching, voids andspaces) on an orthodontic appliance. The output data of the model may beutilized for 3D printing of an orthodontic appliance or for instructinga robot to form or shape an orthodontic appliance or parts thereof, suchas an arch wire. Aspects of the present disclosure relate to systems andmethods used for designing an orthodontic appliance, the parameters,material properties and geometry of said appliance being optimized so asto apply a stimulus that represents an optimal force to one or moreteeth, based at least to some extent on the behavior of the periodontalligament.

The appliance configuration engine (43) described above with referenceto FIG. 1A may for example be configured to perform or conduct a methodfor orthodontic appliance configuration. This may for example includeconducting a method for designing an orthodontic appliance includingconducting a method for preparing a specification for, designing, andproducing an orthodontic clear aligner using critical orthodontic forceparameters or comparable PDL behavior-based data points.

FIG. 39 is a flow diagram which illustrates one example embodiment of amethod for orthodontic appliance configuration according to aspects ofthe present disclosure. The method may be conducted by a suitablecomputing device which in some embodiments may form a part of or mayprovide an appliance configuration engine.

The method may include receiving (590) patient data (13) includingpatient treatment data and patient characteristic data. The patient datamay be received from a point of care system (5) via a communicationnetwork (21) and/or interface component (23). The patient data may forexample be input into a point of care system by a healthcareprofessional responsible for the treatment or correction of thepatient's tooth or teeth. In other embodiments, the patient data (13)may be received from another source, such as a datastore (7) aninterface component (23) or the like.

The patient characteristic data may for example include data pointsrelating to one or more of physiological, biological, genetic andsituational characteristics of the patient. The patient treatment dataincludes one or both of a tooth type indicator and a tooth positionindicator associated with the tooth of the patient to be treated andtreatment movement data which describes the required tooth movement toeffect treatment or correction of the tooth.

In some embodiments, receiving patient data includes determining (592)treatment movement data for the patient. This may for example includeusing a treatment plan engine (41) which determines the treatmentmovement data for one or more stages of treatment. Determining treatmentmovement data may include determining required movement of the toothbased on the tooth's current condition or situation and its desired (ortreated or corrected) condition or situation. Receiving patienttreatment data may for example include receiving a tooth model withindividual teeth and the planned movement of each individual tooth andusing the model to determine the treatment movement value includingdirection and magnitude of movement of each individual tooth.

Receiving patient data thus includes receiving information about theintended movement of a specific tooth. This information may include thetype of tooth and can include other patient data including the patientage, health, geographical location or ethnicity (as contained in thepatient characteristic data). The movement of one or more teeth can bespecified by a treatment plan, which can either be described by aninitial and a final position or can be described by one or more stepsdescribing the transformation of each tooth object in space and in sixdegrees of freedom.

The method may include obtaining (593) a force value. In someembodiments, this may include obtaining a force value, for example astage force value, from the treatment plan engine, the datastore, aninterface module or the like. The stage force value approximates atarget orthodontic force value having been determined based on patientdata and PDL behavior data associated with the patient data, the patientdata including patient treatment data and patient characteristic data,the patient treatment data at least including one or both of a toothtype indicator and a tooth position indicator associated with a tooth ofa patient to be treated and a treatment movement value which describesthe required tooth movement to effect treatment of the tooth.

In other embodiments, the obtaining the force value includes determiningthe force value using the patient data including patient treatment dataand patient characteristic data. This may include determining (594) atarget orthodontic force value based on at least a subset of the patientdata (e.g. using the optimal force engine). For example, determining thetarget force value may include using a PDL behavior model to determineone or more determinate points of PDL behavior data associated with thepatient data inputting the one or more determinate points into analgorithm that determines the target orthodontic force as a function ofthe one or more determinate points.

Determining the target orthodontic force may therefore include definingan optimal force relative to the estimated PDL behavior curve. As hasbeen explained in the foregoing, this can be done by identifying adeterminate point on the curve, for example at which the PDL isestimated to reach a specific compression and defining such a point as acritical force level Fc. A case and tooth specific force can then bedefined as a function of the critical force Fc. In addition, such aforce can be a function of time and thus can vary over the duration oforthodontic treatment and can be different for each orthodonticappliance used throughout the duration of treatment.

The force value may therefore be the target orthodontic force value orthe stage force value received from the treatment planning engine. Themethod includes using (596) the force value to determine configurationparameters for configuring an orthodontic appliance to apply the targetorthodontic force value or stage force value, as the case may be (i.e.,applying a force having magnitude and direction as represented by orcorresponding to the relevant force value). The configuration parametersmay for example include specification for one or more of: stiffness,size, shape and material properties of individual portions or zones ofan orthodontic appliance. In some embodiments, determining configurationparameters includes determining the geometry and material properties ofthe appliance so as to apply the target orthodontic force. In the caseof an orthodontic appliance in the form of an aligner, for example, theconfiguration parameters may specify:

-   -   the thickness of the appliance at specific zones through        specification of size and shape parameters;    -   the inclusion and configuration of features such as surface or        sub-surface features of the appliance (e.g. ridges, cuts, ribs,        bumps, springs, hatching, voids, spaces the like); and,    -   the materials and their associated properties (e.g. rigid        plastic materials, flexible plastic materials a combination of        rigid and flexible plastics materials, etc.).

Referring briefly to FIG. 40, a cross-section through two exampleorthodontic appliances is illustrated, in which a first appliance (602)shown in FIG. 40(a) has a first shape through the section and a secondappliance (604) shown in FIG. 40(b) has a second shape through thesection. In the example embodiments illustrated, the first appliance hasa constant thickness throughout the section, while the second appliancehas different thicknesses at different portions or zones of theappliance. The second appliance for example has a thickened portion orzone (606) which is specifically shaped and dimensioned through theconfiguration parameters to apply the target orthodontic force value orstage force value, as the case may be, relevant to that tooth (608). Thethickened portion or zone may have the effect of applying a greaterforce along the x-axis (610) compared for example to that force thatwould be applied along the x-axis by the appliance illustrated in FIG.40(a). The thickened portion is provided in the lingual part of theappliance to increase the stiffness and act as a structural feature ofthe appliance. Similarly, there is a thicker section (607) near theincisal edge of the tooth to provide a greater stiffness and force thatis applied to the tooth. The figure also shows a thinner section of theappliance toward the facial side of the tooth, which can provide asofter or more flexible function due to this part of the appliancehaving a lower stiffness.

Although not illustrated, an appliance can also be configured throughconfiguration parameters to have different features in differentlocations of the appliance such as ridges, cuts, ribs, bumps, springs,hatching, voids, spaces thicker or thinner selections, extrusions,folds, or any other type of geometric variation. Such variationsdirectly affect the performance and more specifically the stiffness ofthe appliance at the location at which they are placed. By controllingthe geometry of the features and the geometry the stiffness of theappliance can be controlled and thereby the forces applied to the teeth.

In the case of an orthodontic appliance in the form of dental braces(650), for example as illustrated in FIG. 41, configuration parametersmay include stiffness of wires (652, 654), which may in turn beconfigured through material properties, wire thickness and the like. Theproperties of the wire may change along its length such that differentforces are applied to different teeth so as to apply a case, patient andtooth specific force to each tooth for optimal treatment. for example,the wire may have a first thickness for a first portion (658) thereof, asecond thickness for a second portion (660) thereof and so on so as toapply a case, patient and tooth specific force to each tooth for optimaltreatment. Configuration parameters may also include specification of anelastic band (656) (e.g. location, flexibility, thickness, etc.).Further configuration parameters may include the shape of the wire andmore specifically bending of the wire along the arch as well as twistingof the wire to create torsion. These configuration parameters may beimplemented by a robotic arch wire bending robot to shape the arch wire.

Returning to FIG. 39, the method includes outputting (598) theconfiguration parameters. The configuration parameters may be output toone or more of: a treatment plan engine (41); a point of care system(5); an interface module (23); and, a datastore (7). The method may beconducted or repeated for each tooth to be treated such that a tooth-and patient-specific configuration parameters are determined for eachtooth to be treated. Outputting the configuration parameters may includeoutputting the configuration parameters for each tooth of the patient tobe treated or corrected to an additive manufacturing machine tomanufacture an orthodontic appliance configured to apply the targetorthodontic force value or stage force value, as the case may be, to theteeth. For example, in some embodiments, outputting the configurationparameters includes outputting a 3D mesh or point cloud file (such as astereolithography (STL) file) embodying the configuration parameterssuch that the file can be input into the additive manufacturing (or 3Dprinting) machine to manufacture an orthodontic appliance configured toapply the relevant target orthodontic force valued or stage forcevalued, as the case may be. As mentioned, each force value has acomponent for each of six degrees of freedom such that the differentportions or zones of the orthodontic appliance will be shaped/configureddifferently to apply different forces in different directions/rotations.

In some embodiments, and referring now to FIG. 42, the method includesevaluating the output configuration parameters, including configuring(618) an orthodontic appliance in accordance with the configurationparameters (e.g. including setting an appliance geometry based on theconfiguration parameters), testing (620) the appliance to evaluate thedifference between an actual orthodontic force applied by the applianceand the target orthodontic force value or stage force value, as the casemay be (or target stiffness versus actual stiffness) and, if (621) theapplied orthodontic force does not approximate the target orthodonticforce value or stage force value, as the case may be, the methodincludes updating (622) the configuration parameters based on thisdifference. The evaluation may repeat until the applied orthodonticforce approximates the target orthodontic force at which point theconfiguration parameters may be stored (624) as the final configurationparameters.

Aspects of the present disclosure therefore enable control ofconfiguration parameters of an orthodontic appliance based on an optimalor target orthodontic force value. This allows for orthodontic treatmentor correction based on an optimal orthodontic force that is determinedbased on PDL behavior data expected for that specific tooth of thespecific patient. As mentioned in the foregoing, the PDL behavior datadescribes the force vs displacement relationship of the PDL. Aspects ofthe present disclosure may therefore improve appliance configurationtechnology b determining target orthodontic forces based on PDL datapoints that are patient, case and tooth specific. Aspects of the presentdisclosure enable the determination of orthodontic applianceconfiguration parameters using PDL behavior data which includes or isbased on measurement data obtained from a large number of human oranimal subjects in-vivo.

The appliance configuration engine (43) may therefore conduct a methodfor orthodontic appliance configuration including: receiving thedirection and amount of movement of one or more teeth; determining thePDL behavior for that specific direction in three dimensional space;using a machine learning model that has been trained to provide anestimate of the force vs displacement relationship of the PDL, thebehavior being based at least to some extent on the PDL behaviormeasured in-vivo; with reference to the PDL behavior in the givendirection, determining a target or optimal orthodontic force; based onthe determined target or optimal force, in the given direction, definingthe desired appliance function; and, based on the desired appliancefunction, determining the size, shape and material properties of theorthodontic appliance.

Orthodontic Treatment System

Various modules may be provided for implementing the methods describedabove. FIG. 43 is a block diagram which illustrates exemplary moduleswhich may be provided by an orthodontic treatment system (1) which mayinclude a computing device (801).

The computing device (801) may include a processor (802) for executingthe functions of modules described below, which may be provided byhardware or by software units executing on the computing device (801).The software units may be stored in a memory (804) and instructions maybe provided to the processor (802) to carry out the functionality of thedescribed modules. In some cases, for example in a cloud computingimplementation, software units arranged to manage and/or process data onbehalf of the computing device (801) may be provided remotely.

The system (1) may include a patient data receiving module (806) forreceiving patient data associated with a patient. The patient dataincludes patient characteristic data and patient treatment data, thepatient treatment data at least including one or both of a tooth typeindicator and a tooth position indicator associated with a tooth of thepatient to be corrected.

The system (1) may include a PDL behavior data retrieving module (808)for retrieving PDL behavior data associated with at least a subset ofthe patient data. As mentioned, the PDL behavior data includes or isbased on force measurement data points and corresponding displacementmeasurement data points having been measured in-vivo while applying aforce to a tooth of a human or animal subject.

The system (1) may include a target orthodontic force determining module(810) for determining a target orthodontic force value that is specificto the patient data by inputting the PDL behavior data into an algorithmwhich determines the target orthodontic force based on the PDL behaviordata.

The system (1) may include a feedback receiving module (811A) forreceiving feedback data obtained during use of an orthodontic applianceconfigured to apply a target orthodontic force to a tooth of a patient.The system (1) may include an algorithm updating module (811B) forupdating the algorithm using the feedback data. The target orthodonticforce determining module (810) may further be for using the updatedalgorithm to determine an updated value corresponding to an updatedtarget orthodontic force for use in reconfiguring or replacing theorthodontic appliance so as to apply the updated target orthodonticforce.

The system (1) may include a target orthodontic force outputting module(812) for outputting the target orthodontic force value.

The system (1) may include a dataset obtaining module (814) forobtaining a dataset including or based on one or both of measured PDLbehavior data and associated metadata. The measured PDL behavior dataincludes force measurement data points and corresponding displacementmeasurement data points having been measured in-vivo while applying aforce to a particular tooth of a human or animal subject. The metadatarelating to the human or animal subject and including data pointsrelating to one or more of physiological, biological and geneticcharacteristics of the human or animal subject.

The system (1) may include a model training module (816) for training amodel using a learning algorithm which identifies patterns in orrelationships between data points of the dataset. Training the modulemay include identifying a relationship between force measurement datapoints and displacement measurement data points.

The system (1) may include a model outputting module (818) foroutputting the model for use in determining PDL behavior data pointsincluding required force data points for a desired displacement of atooth of a patient or resultant displacement data points for an appliedforce to a tooth of a patient.

The PDL behavior curves illustrated in the accompanying Figures showexample measurements for a single tooth of a single human/animal subjectand for one of six possible degrees of freedom. It should be appreciatedthat for each human/animal subject, there may be obtained one PDLbehavior curve for each tooth of the patient and for each of the sixpossible degrees of freedom.

The system (1) may include a stage value determining module (822) fordetermining one or more stage movement values and corresponding stageforce values. Each stage force value is based on a target orthodonticforce value that is based on one or more determinate points of PDLbehavior data associated with at least a subset of the patient data. Thesum of the one or more stage movement values approximates the treatmentmovement value received with the patient data.

The system (1) may include a stage value output module (824) foroutputting the one or more stage movement values and corresponding stageforce values as a treatment plan for treatment of the tooth.

The system (1) may include a force value obtaining module (826) forobtaining a force value which approximates a target orthodontic forcevalue having been determined based on patient data and PDL behavior dataassociated with the patient data.

The system (1) may include a configuration parameter determining module(828) for using the force value to determine configuration parametersfor configuring an orthodontic appliance to apply the force value (i.e.,applying a force having magnitude and direction as represented by orcorresponding to the force value) to the tooth of the patient.

The system (1) may include a configuration parameter output module (830)for outputting the configuration parameters.

FIG. 44 illustrates an example of a computing device (2900) in whichvarious aspects of the disclosure may be implemented. The computingdevice (2900) may be embodied as any form of data processing deviceincluding a personal computing device (e.g. laptop or desktop computer),a server computer (which may be self-contained, physically distributedover a number of locations), a client computer, or a communicationdevice, such as a mobile phone (e.g. cellular telephone), satellitephone, tablet computer, personal digital assistant or the like.Different embodiments of the computing device may dictate the inclusionor exclusion of various modules or subsystems described below.

The computing device (2900) may be suitable for storing and executingcomputer program code. The various participants and elements in thepreviously described system diagrams may use any suitable number ofsubsystems or modules of the computing device (2900) to facilitate thefunctions described herein. The computing device (2900) may includesubsystems or modules interconnected via a communication infrastructure(2905) (for example, a communications bus, a network, etc.). Thecomputing device (2900) may include one or more processors (2910) and atleast one memory module in the form of computer-readable media. The oneor more processors (2910) may include one or more of: CPUs, graphicalprocessing units (GPUs), microprocessors, field programmable gate arrays(FPGAs), application specific integrated circuits (ASICs) and the like.In some configurations, a number of processors may be provided and maybe arranged to carry out calculations simultaneously. In someimplementations various subsystems or modules of the computing device(2900) may be distributed over a number of physical locations (e.g. in adistributed, cluster or cloud-based computing configuration) andappropriate software units may be arranged to manage and/or process dataon behalf of remote devices.

The memory modules may include system memory (2915), which may includeread only memory (ROM) and random access memory (RAM). A basicinput/output system (BIOS) may be stored in ROM. System software may bestored in the system memory (2915) including operating system software.The memory modules may also include secondary memory (2920). Thesecondary memory (2920) may include a fixed disk (2921), such as a harddisk drive, and, optionally, one or more storage interfaces (2922) forinterfacing with storage modules (2923), such as removable storagemodules (e.g. magnetic tape, optical disk, flash memory drive, externalhard drive, removable memory chip, etc.), network attached storagemodules (e.g. NAS drives), remote storage modules (e.g. cloud-basedstorage) or the like.

The computing device (2900) may include an external communicationsinterface (2930) for operation of the computing device (2900) in anetworked environment enabling transfer of data between multiplecomputing devices (2900) and/or the Internet. Data transferred via theexternal communications interface (2930) may be in the form of signals,which may be electronic, electromagnetic, optical, radio, or other typesof signal. The external communications interface (2930) may enablecommunication of data between the computing device (2900) and othercomputing devices including servers and external storage facilities. Webservices may be accessible by and/or from the computing device (2900)via the communications interface (2930).

The external communications interface (2930) may be configured forconnection to wireless communication channels (e.g., a cellulartelephone network, wireless local area network (e.g. using Wi-Fi™),satellite-phone network, Satellite Internet Network, etc.) and mayinclude an associated wireless transfer element, such as an antenna andassociated circuitry.

The computer-readable media in the form of the various memory modulesmay provide storage of computer-executable instructions, datastructures, program modules, software units and other data. A computerprogram product may be provided by a computer-readable medium havingstored computer-readable program code executable by the centralprocessor (2910). A computer program product may be provided by anon-transient or non-transitory computer-readable medium, or may beprovided via a signal or other transient or transitory means via thecommunications interface (2930).

Interconnection via the communication infrastructure (2905) allows theone or more processors (2910) to communicate with each subsystem ormodule and to control the execution of instructions from the memorymodules, as well as the exchange of information between subsystems ormodules. Peripherals (such as printers, scanners, cameras, or the like)and input/output (I/O) devices (such as a mouse, touchpad, keyboard,microphone, touch-sensitive display, input buttons, speakers and thelike) may couple to or be integrally formed with the computing device(2900) either directly or via an I/O controller (2935). One or moredisplays (2945) (which may be touch-sensitive displays) may be coupledto or integrally formed with the computing device (2900) via a displayor video adapter (2940).

The foregoing description has been presented for the purpose ofillustration; it is not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Persons skilled in therelevant art can appreciate that many modifications and variations arepossible in light of the above disclosure.

Any of the steps, operations, modules or processes described herein maybe performed or implemented with one or more hardware or software units,alone or in combination with other devices. In one embodiment, asoftware unit is implemented with a computer program product comprisinga non-transient or non-transitory computer-readable medium containingcomputer program code, which can be executed by a processor forperforming any or all of the steps, operations, or processes described.Software units or functions described in this application may beimplemented as computer program code using any suitable computerlanguage such as, for example, Python, Java™, C++, or Perl™ using, forexample, conventional or object-oriented techniques. The computerprogram code may be stored as a series of instructions, or commands on anon-transitory computer-readable medium, such as a random access memory(RAM), a read-only memory (ROM), a magnetic medium such as a hard-drive,or an optical medium such as a CD-ROM. Any such computer-readable mediummay also reside on or within a single computational apparatus, and maybe present on or within different computational apparatuses within asystem or network.

Flowchart illustrations and block diagrams of methods, systems, andcomputer program products according to embodiments are used herein. Eachblock of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, may provide functions which may be implemented by computerreadable program instructions. In some alternative implementations, thefunctions identified by the blocks may take place in a different orderto that shown in the flowchart illustrations.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations, such as accompanying flow diagrams, are commonly usedby those skilled in the data processing arts to convey the substance oftheir work effectively to others skilled in the art. These operations,while described functionally, computationally, or logically, areunderstood to be implemented by computer programs or equivalentelectrical circuits, microcode, or the like. The described operationsmay be embodied in software, firmware, hardware, or any combinationsthereof.

The language used in the specification has been principally selected forreadability and instructional purposes, and it may not have beenselected to delineate or circumscribe the inventive subject matter. Itis therefore intended that the scope of the invention be limited not bythis detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention set forth in any accompanying claims.

Finally, throughout the specification and any accompanying claims,unless the context requires otherwise, the word ‘comprise’ or variationssuch as ‘comprises’ or ‘comprising’ will be understood to imply theinclusion of a stated integer or group of integers but not the exclusionof any other integer or group of integers.

REFERENCES

-   1. Ren Y, Maltha J C, Van 't Hof M, Kuijpers-Jagtman A M. Optimum    force magnitude for orthodontic tooth movement: a mathematic model.    Am. J. Orthod. Dentofac. Orthop. 2004; 125:71-7.-   2. Quinn R S, Yoshikawa D K. A reassessment of force magnitude in    orthodontics. Am. J. Orthod. 1985; 88:252-60.-   3. Bosshardt D D, Bergomi M, Vaglio G, Wiskott A. Regional    structural characteristics of bovine periodontal ligament samples    and their suitability for biomechanical tests. J. Anat. 2008;    212:319-29.-   4. Cattaneo P M, Dalstra M, Melsen B. The finite element method: a    tool to study orthodontic tooth movement. J. Dent. Res. 2005;    84:428-33.-   5. Iwasaki L R, Haack J E, Nickel J C, Morton J. Human tooth    movement in response to continuous stress of low magnitude. Am. J.    Orthod. Dentofac. Orthop. 2000; 117:175-83.-   6. Owman-Moll P, Kurol J, Lundgren D. Effects of a doubled    orthodontic force magnitude on tooth movement and root resorptions.    An inter-individual study in adolescents. Eur. J. Orthod. 1996;    18:141-50.-   7. Ren Y, Maltha J C, Kuijpers-Jagtman A M. Optimum force magnitude    for orthodontic tooth movement: a systematic literature review.    Angle Orthod. 2003; 73:86-92.-   8. van Leeuwen E J, Maltha J C, Kuijpers-Jagtman A M. Tooth movement    with light continuous and discontinuous forces in beagle dogs.    Eur. J. Oral Sci. 1999; 107:468-74.-   9. Castellini P, Scalise L, Tomasini E P. Teeth mobility    measurement: a laser vibrometry approach. J. Clin. Laser Med. Surg.    1998; 16:269-72.-   10. Yoshida N, Koga Y, Kobayashi K, Yamada Y, Yoneda T. A new method    for qualitative and quantitative evaluation of tooth displacement    under the application of orthodontic forces using magnetic sensors.    Med. Eng. Phys. 2000; 22:293-300.-   11. Ziegler A, Keilig L, Kawarizadeh A, Jager A, Bourauel C.    Numerical simulation of the biomechanical behaviour of multi-rooted    teeth. Eur. J. Orthod. 2005; 27:333-9.-   12. Sanctuary C S, Wiskott H W A, Justiz J, Botsis J, Belser U C. In    vitro time-dependent response of periodontal ligament to mechanical    loading. J. Appl. Physiol. 2005; 99:2369-78.-   13. Papadopoulou K, Keilig L, Eliades T, Krause R, Jager A,    Bourauel C. The time-dependent biomechanical behaviour of the    periodontal ligament—An in vitro experimental study in minipig    mandibular two-rooted premolars. Eur. J. Orthod. 2014; 36:9-15.-   14. Jing Y, Han X L, Cheng B H, Bai D. Three-dimensional FEM    analysis of stress distribution in dynamic maxillary canine    movement. Chinese Sci. Bull. 2013; 58:2454-9.-   15. Papadopoulou K, Hasan I, Keilig L, et al. Biomechanical time    dependency of the periodontal ligament: A combined experimental and    numerical approach. Eur. J. Orthod. 2013; 35:811-8.-   16. Minch L. Material properties of periodontal ligaments. Postepy    Hig. Med. Dosw. 2013; 67:1261-4.-   17. Wakabayashi N, Ona M, Suzuki T, Igarashi Y. Nonlinear finite    element analyses: advances and challenges in dental applications. J.    Dent. 2008; 36:463-71.-   18. Malemann H R. Tooth mobility: a review of clinical aspects and    research findings. J. Periodontol. 1967; 38: Suppl:686-713.-   19. Von Bohl M, Maltha J C, Von Den Hoff H, Kuijpers-Jagtman A M.    Changes in the periodontal ligament after experimental tooth    movement using high and low continuous forces in beagle dogs. Angle    Orthod. 2004; 74:16-25.-   20. Natali A N, Pavan P G, Scarpa C. Numerical analysis of tooth    mobility: Formulation of a non-linear constitutive law for the    periodontal ligament. Dent. Mater. 2004; 20:623-9.-   21. Melsen B, Cattaneo P M, Dalstra M, Kraft D C. The Importance of    Force Levels in Relation to Tooth Movement. Semin. Orthod. 2007;    13:220-33.-   22. Kojima Y, Fukui H. Numerical simulations of canine retraction    with T-loop springs based on the updated moment-to-force ratio.    Eur. J. Orthod. 2012; 34:10-8.-   23. Cattaneo P M, Dalstra M, Melsen B. Moment-to-force ratio, center    of rotation, and force level: a finite element study predicting    their interdependency for simulated orthodontic loading regimens.    Am. J. Orthod. Dentofacial Orthop. 2008; 133:681-9.-   24. Kawarizadeh A, Bourauel C, Jager A. Experimental and numerical    determination of initial tooth mobility and material properties of    the periodontal ligament in rat molar specimens. Eur. J. Orthod.    2003; 25:569-78.

1. A method comprising: obtaining feedback data including patientfeedback data points collected by fitting an orthodontic applianceconfigured to apply a force to a tooth of a patient and indicating alevel of discomfort experienced by the patient, wherein the force is afunction of a critical force that is specific to the patient;correlating the level of discomfort to the force applied to the tooth ofthe patient; and, determining whether or not the feedback data isoptimized for determining a target orthodontic force for orthodontictreatment that is an optimal orthodontic force based on the criticalforce.
 2. The method as claimed in claim 1, wherein the critical forceis based on periodontal ligament (PDL) behavior data.
 3. The method asclaimed in claim 1, wherein the level of discomfort is indicated on ascale with relation to the force as a function of the critical force. 4.The method as claimed in claim 1, wherein the feedback data is obtainedduring use or testing of the orthodontic appliance.
 5. The method asclaimed in claim 1, wherein the feedback data is based on or includesrate of change data points measured while using the orthodonticappliance.
 6. The method as claimed in claim 1, wherein the feedbackdata includes treatment feedback data points having been obtained fromuse of the orthodontic appliance.
 7. The method as claimed in claim 1,wherein the optimal orthodontic force achieves the least level ofdiscomfort while still resulting in orthodontic tooth movement.
 8. Themethod as claimed in claim 1, wherein the optimal orthodontic forceoptimizes a balance of patient comfort and treatment outcome.
 9. Themethod as claimed in claim 1, wherein the method repeats to obtainupdated feedback data until the feedback data points of the feedbackdata are optimized.
 10. The method as claimed in claim 1, includingdetermining the target orthodontic force by inputting the critical forceinto an algorithm.
 11. The method as claimed in claim 10, wherein thealgorithm includes one or more of time, magnitude, patientcharacteristic and patient treatment components.
 12. The method asclaimed in claim 10, including using the target orthodontic force todetermine one or both of a stage force value and a stage movement valuefor one or more stages of orthodontic treatment.
 13. The method asclaimed in claim 10, including using the target orthodontic force todetermine configuration parameters for configuring an orthodonticappliance to apply a force which approximates the target orthodonticforce.
 14. The method as claimed in claim 13, wherein the force thatapproximates the target orthodontic force is a stage force value. 15.The method as claimed in claim 1, wherein the target orthodontic forceis determined by inputting the critical force into an algorithm, themethod including: if the feedback data is not optimized, updating thealgorithm for determining an updated target orthodontic force; or, ifthe feedback data is optimized, storing the algorithm as an optimalalgorithm for use in determining a target orthodontic force that is theoptimal orthodontic force.
 16. The method as claimed in claim 15,wherein the updated target orthodontic force is used to reconfigure theappliance or create a new appliance configured to apply the updatedtarget orthodontic force.
 17. The method as claimed in claim 15, whereinupdating the algorithm iteratively updates the algorithm to optimize thefeedback data.
 18. The method as claimed in claim 15, wherein updatingthe algorithm includes adjusting weights of one or more components orvariables of the algorithm.